Oracle DBMS_DATA_MINING
Version 21c

General Information
Library Note Morgan's Library Page Header
ACE Director Alum Daniel Morgan, founder of Morgan's Library, is scheduling complimentary technical Workshops on Database Security for the first 30 Oracle Database customers located anywhere in North America, EMEA, LATAM, or APAC that send an email to asra_us@oracle.com. Request a Workshop for your organization today.
Purpose DBMS_DATA_MINING provides routines for Data Mining operations in an Oracle Server supporting both supervised and unsupervised data mining. Supervised data mining predicts a target value based on historical data. Unsupervised data mining discovers natural groupings and does not use a target. You can use Oracle Data Mining to mine structured data and unstructured text.

Supervised data mining functions include:
  • Classification
  • Regression
  • Feature Selection (Attribute Importance)

Unsupervised data mining functions include:
  • Clustering
  • Association
  • Feature Extraction
  • Anomaly Detection
AUTHID CURRENT_USER
Constants -- General Settings - Begin ----------------------------------------

-- Data Prep: Setting Names
prep_auto CONSTANT VARCHAR2(30) := 'PREP_AUTO';

-- Data Prep: Setting Values for prep_auto
prep_auto_off CONSTANT VARCHAR2(30) := 'OFF';
prep_auto_on CONSTANT VARCHAR2(30)  := 'ON';

-- normalization settings
-- 2D numeric columns scale
prep_scale_2dnum CONSTANT VARCHAR2(30)  := 'PREP_SCALE_2DNUM';
-- values for prep_scale_2dnum
prep_scale_stddev CONSTANT VARCHAR2(30) := 'PREP_SCALE_STDDEV';
prep_scale_range CONSTANT VARCHAR2(30) := 'PREP_SCALE_RANGE';
-- nested numeric columns scale
prep_scale_nnum CONSTANT VARCHAR2(30) := 'PREP_SCALE_NNUM';
-- value for prep_scale_nnum
prep_scale_maxabs CONSTANT VARCHAR2(30) := 'PREP_SCALE_MAXABS';
-- 2D numeric shift
prep_shift_2dnum CONSTANT VARCHAR2(30) := 'PREP_SHIFT_2DNUM';
-- values for prep_shift_2dnum
prep_shift_mean CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MEAN';
prep_shift_min CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MIN';

-- Score Criterion Type: Setting Values for score_criterion_type
score_criterion_probability CONSTANT VARCHAR2(30) := 'PROBABILITY';
score_criterion_cost CONSTANT VARCHAR2(30) := 'COST';

-- Row Weights - Setting Name
odms_row_weight_column_name CONSTANT VARCHAR2(30) := 'ODMS_ROW_WEIGHT_COLUMN_NAME';

-- Cost Matrix
cost_matrix_type_score CONSTANT VARCHAR2(30) := 'SCORE';
cost_matrix_type_create CONSTANT VARCHAR2(30) := 'CREATE';

-- Missing Value Treatment - Setting Name
odms_missing_value_treatment CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_TREATMENT';

-- Missing Value Treatment: Setting Values for ODMS_MISSING_VALUE_TREATMENT
odms_missing_value_mean_mode CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_MEAN_MODE';
odms_missing_value_delete_row CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_DELETE_ROW';
odms_missing_value_auto CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_AUTO';

-- Transactional training data format: Setting Names
odms_item_id_column_name CONSTANT VARCHAR2(30) := 'ODMS_ITEM_ID_COLUMN_NAME';
odms_item_value_column_name CONSTANT VARCHAR2(30) := 'ODMS_ITEM_VALUE_COLUMN_NAME';

-- Unstructured Text Setting Names
odms_text_policy_name CONSTANT VARCHAR2(30) := 'ODMS_TEXT_POLICY_NAME';
odms_text_max_features CONSTANT VARCHAR2(30) := 'ODMS_TEXT_MAX_FEATURES';
odms_text_min_documents CONSTANT VARCHAR2(30) := 'ODMS_TEXT_MIN_DOCUMENTS';

-- Approximate computation
odms_approximate_computation CONSTANT VARCHAR2(30) := 'ODMS_APPROXIMATE_COMPUTATION';
-- Setting values for odms_approximate_computation
odms_appr_comp_enable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_ENABLE';
odms_appr_comp_disable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_DISABLE';

-- Sampling
odms_sampling CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING';
-- Setting values for odms_sampling
odms_sampling_enable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_ENABLE';
odms_sampling_disable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_DISABLE';

-- Sample size
odms_sample_size CONSTANT VARCHAR2(30) := 'ODMS_SAMPLE_SIZE';

-- Partitioning
odms_partition_columns CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_COLUMNS';

-- Max partition columns
odms_max_partitions CONSTANT VARCHAR2(30) := 'ODMS_MAX_PARTITIONS';

-- Max sup bins ---
clas_max_sup_bins CONSTANT VARCHAR2(30) := 'CLAS_MAX_SUP_BINS';

--Partition build type (inter/intra/hybrid)
odms_partition_build_type CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_TYPE';
odms_partition_build_inter CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_INTER';
odms_partition_build_intra CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_INTRA';
odms_partition_build_hybrid CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_HYBRID';

-- random seed
odms_random_seed CONSTANT VARCHAR2(30):= 'ODMS_RANDOM_SEED';

-- retain information for details (default is enable)
odms_details CONSTANT VARCHAR2(30):= 'ODMS_DETAILS';
odms_enable CONSTANT VARCHAR2(30):= 'ODMS_ENABLE';
odms_disable CONSTANT VARCHAR2(30):= 'ODMS_DISABLE';

-- override default tablespace
odms_tablespace_name CONSTANT VARCHAR2(30):= 'ODMS_TABLESPACE_NAME';

-- General Settings - End -------------------------------------------------

----------- Function and Algorithm Settings - Begin ---------------------

-- FUNCTION NAME (input as CREATE_MODEL parameter)
--
classification CONSTANT VARCHAR2(30) := 'CLASSIFICATION';
regression CONSTANT VARCHAR2(30) := 'REGRESSION';
clustering CONSTANT VARCHAR2(30) := 'CLUSTERING';
association CONSTANT VARCHAR2(30) := 'ASSOCIATION';
feature_extraction CONSTANT VARCHAR2(30) := 'FEATURE_EXTRACTION';
attribute_importance CONSTANT VARCHAR2(30) := 'ATTRIBUTE_IMPORTANCE';
time_series CONSTANT VARCHAR2(30) := 'TIME_SERIES';

-- FUNCTION: Setting Names (input to settings_name column in settings table)
clas_priors_table_name CONSTANT VARCHAR2(30) := 'CLAS_PRIORS_TABLE_NAME';
clas_weights_table_name CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_TABLE_NAME';
clas_cost_table_name CONSTANT VARCHAR2(30) := 'CLAS_COST_TABLE_NAME';
-- Balanced weights (boolean: on/off) */
clas_weights_balanced CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_BALANCED';
clas_weights_bal_off CONSTANT VARCHAR2(30) := 'OFF';
clas_weights_bal_on CONSTANT VARCHAR2(30) := 'ON';

-- AR: Setting Names
asso_max_rule_length CONSTANT VARCHAR2(30) := 'ASSO_MAX_RULE_LENGTH';
asso_min_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_CONFIDENCE';
asso_min_support CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT';
asso_min_support_int CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT_INT';
asso_min_rev_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_REV_CONFIDENCE';
asso_in_rules CONSTANT VARCHAR2(30) := 'ASSO_IN_RULES';
asso_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_EX_RULES';
asso_ant_in_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_IN_RULES';
asso_ant_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_EX_RULES';
asso_cons_in_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_IN_RULES';
asso_cons_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_EX_RULES';
asso_aggregates CONSTANT VARCHAR2(30) := 'ASSO_AGGREGATES';
asso_abs_error CONSTANT VARCHAR2(30) := 'ASSO_ABS_ERROR';
asso_conf_level CONSTANT VARCHAR2(30) := 'ASSO_CONF_LEVEL';

feat_num_features CONSTANT VARCHAR2(30) := 'FEAT_NUM_FEATURES';
clus_num_clusters CONSTANT VARCHAR2(30) := 'CLUS_NUM_CLUSTERS';

-- ALGORITHM Setting Name (input to settings_name column in settings table)
algo_name CONSTANT VARCHAR2(30) := 'ALGO_NAME';

-- ALGORITHM: Setting Values for algo_name
algo_naive_bayes CONSTANT VARCHAR2(30) := 'ALGO_NAIVE_BAYES';
algo_adaptive_bayes_network CONSTANT VARCHAR2(30) := 'ALGO_ADAPTIVE_BAYES_NETWORK';
algo_support_vector_machines CONSTANT VARCHAR2(30) := 'ALGO_SUPPORT_VECTOR_MACHINES';
algo_nonnegative_matrix_factor CONSTANT VARCHAR2(30) := 'ALGO_NONNEGATIVE_MATRIX_FACTOR';
algo_apriori_association_rules CONSTANT VARCHAR2(30) := 'ALGO_APRIORI_ASSOCIATION_RULES';
algo_kmeans CONSTANT VARCHAR2(30) := 'ALGO_KMEANS';
algo_ocluster CONSTANT VARCHAR2(30) := 'ALGO_O_CLUSTER';
algo_ai_mdl CONSTANT VARCHAR2(30) := 'ALGO_AI_MDL';
algo_decision_tree CONSTANT VARCHAR2(30) := 'ALGO_DECISION_TREE';
algo_random_forest CONSTANT VARCHAR2(30) := 'ALGO_RANDOM_FOREST';
algo_generalized_linear_model CONSTANT VARCHAR2(30) := 'ALGO_GENERALIZED_LINEAR_MODEL';
algo_singular_value_decomp CONSTANT VARCHAR2(30) := 'ALGO_SINGULAR_VALUE_DECOMP';
algo_expectation_maximization CONSTANT VARCHAR2(30) := 'ALGO_EXPECTATION_MAXIMIZATION';
algo_explicit_semantic_analys CONSTANT VARCHAR2(30) := 'ALGO_EXPLICIT_SEMANTIC_ANALYS';
algo_neural_network CONSTANT VARCHAR2(30) := 'ALGO_NEURAL_NETWORK';
algo_cur_decomposition CONSTANT VARCHAR2(30) := 'ALGO_CUR_DECOMPOSITION';
algo_exponential_smoothing CONSTANT VARCHAR2(30) := 'ALGO_EXPONENTIAL_SMOOTHING';
algo_mset_sprt CONSTANT VARCHAR2(30) := 'ALGO_MSET_SPRT';
algo_xgboost CONSTANT VARCHAR2(30) := 'ALGO_XGBOOST';

-- ALGORITHM SETTINGS AND VALUES
--
-- ABN: Setting Names
abns_model_type CONSTANT VARCHAR2(30) := 'ABNS_MODEL_TYPE';
abns_max_build_minutes CONSTANT VARCHAR2(30) := 'ABNS_MAX_BUILD_MINUTES';
abns_max_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_PREDICTORS';
abns_max_nb_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_NB_PREDICTORS';

-- ABN: Setting Values for abns_model_type
abns_multi_feature CONSTANT VARCHAR2(30) := 'ABNS_MULTI_FEATURE';
abns_single_feature CONSTANT VARCHAR2(30) := 'ABNS_SINGLE_FEATURE';
abns_naive_bayes CONSTANT VARCHAR2(30) := 'ABNS_NAIVE_BAYES';

-- NB: Setting Names
nabs_pairwise_threshold CONSTANT VARCHAR2(30) := 'NABS_PAIRWISE_THRESHOLD';
nabs_singleton_threshold CONSTANT VARCHAR2(30) := 'NABS_SINGLETON_THRESHOLD';

-- SVM: Setting Names
-- NOTE: svms_epsilon applies only for SVM Regression
-- svms_complexity_factor applies to both
-- svms_std_dev applies only for Gaussian Kernels
-- kernel_cache_size to Gaussian kernels only
svms_conv_tolerance CONSTANT VARCHAR2(30) := 'SVMS_CONV_TOLERANCE';
svms_std_dev CONSTANT VARCHAR2(30) := 'SVMS_STD_DEV';
svms_complexity_factor CONSTANT VARCHAR2(30) := 'SVMS_COMPLEXITY_FACTOR';
svms_kernel_cache_size CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_CACHE_SIZE';
svms_epsilon CONSTANT VARCHAR2(30) := 'SVMS_EPSILON';
svms_kernel_function CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_FUNCTION';
svms_active_learning CONSTANT VARCHAR2(30) := 'SVMS_ACTIVE_LEARNING';
svms_outlier_rate CONSTANT VARCHAR2(30) := 'SVMS_OUTLIER_RATE';
svms_num_iterations CONSTANT VARCHAR2(30) := 'SVMS_NUM_ITERATIONS';
svms_num_pivots CONSTANT VARCHAR2(30) := 'SVMS_NUM_PIVOTS';
svms_batch_rows CONSTANT VARCHAR2(30) := 'SVMS_BATCH_ROWS';
svms_regularizer CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER';
svms_solver CONSTANT VARCHAR2(30) := 'SVMS_SOLVER';

-- SVM: Setting Values for svms_kernel_function
svms_linear CONSTANT VARCHAR2(30) := 'SVMS_LINEAR';
svms_gaussian CONSTANT VARCHAR2(30) := 'SVMS_GAUSSIAN';

-- SVM: Setting Values for svms_active_learning
svms_al_enable CONSTANT VARCHAR2(30) := 'SVMS_AL_ENABLE';
svms_al_disable CONSTANT VARCHAR2(30) := 'SVMS_AL_DISABLE';

-- SVM: Setting Values for svms_regularizer
svms_regularizer_l1 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L1';
svms_regularizer_l2 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L2';

-- SVM: Setting Values for svms_solver
svms_solver_sgd CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_SGD';
svms_solver_ipm CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_IPM';

-- KMNS: Setting Names
kmns_distance CONSTANT VARCHAR2(30) := 'KMNS_DISTANCE';
kmns_iterations CONSTANT VARCHAR2(30) := 'KMNS_ITERATIONS';
kmns_conv_tolerance CONSTANT VARCHAR2(30) := 'KMNS_CONV_TOLERANCE';
kmns_split_criterion CONSTANT VARCHAR2(30) := 'KMNS_SPLIT_CRITERION';
kmns_min_pct_attr_support CONSTANT VARCHAR2(30):= 'KMNS_MIN_PCT_ATTR_SUPPORT';
kmns_block_growth CONSTANT VARCHAR2(30) := 'KMNS_BLOCK_GROWTH';
kmns_num_bins CONSTANT VARCHAR2(30) := 'KMNS_NUM_BINS';
kmns_details CONSTANT VARCHAR2(30) := 'KMNS_DETAILS';
kmns_random_seed CONSTANT VARCHAR2(30) := 'KMNS_RANDOM_SEED';

-- KMNS: Setting Values for kmns_distance
kmns_euclidean CONSTANT VARCHAR2(30) := 'KMNS_EUCLIDEAN';
kmns_cosine CONSTANT VARCHAR2(30) := 'KMNS_COSINE';
kmns_fast_cosine CONSTANT VARCHAR2(30) := 'KMNS_FAST_COSINE';

-- KMNS: Setting Values for kmns_split_criterion
kmns_size CONSTANT VARCHAR2(30) := 'KMNS_SIZE';
kmns_variance CONSTANT VARCHAR2(30) := 'KMNS_VARIANCE';

-- KMNS: Setting Values for kmns_details
kmns_details_none CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_NONE';
kmns_details_hierarchy CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_HIERARCHY';
kmns_details_all CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_ALL';

-- NMF: Setting Names
nmfs_num_iterations CONSTANT VARCHAR2(30) := 'NMFS_NUM_ITERATIONS';
nmfs_conv_tolerance CONSTANT VARCHAR2(30) := 'NMFS_CONV_TOLERANCE';
nmfs_random_seed CONSTANT VARCHAR2(30) := 'NMFS_RANDOM_SEED';
nmfs_nonnegative_scoring CONSTANT VARCHAR2(30) := 'NMFS_NONNEGATIVE_SCORING';
-- Setting values for NMFS_NONNEGATIVE_SCORING
nmfs_nonneg_scoring_enable CONSTANT VARCHAR2(30) := 'NMFS_NONNEG_SCORING_ENABLE';
nmfs_nonneg_scoring_disable CONSTANT VARCHAR2(30) := 'NMFS_NONNEG_SCORING_DISABLE';

-- OCLT: Setting Names for O-Cluster
oclt_sensitivity CONSTANT VARCHAR2(30) := 'OCLT_SENSITIVITY';
oclt_max_buffer CONSTANT VARCHAR2(30) := 'OCLT_MAX_BUFFER';

-- TREE: Setting Names
tree_impurity_metric CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_METRIC';
tree_term_max_depth CONSTANT VARCHAR2(30) := 'TREE_TERM_MAX_DEPTH';
tree_term_minrec_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_SPLIT';
tree_term_minpct_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_SPLIT';
tree_term_minrec_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_NODE';
tree_term_minpct_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_NODE';

-- TREE: Setting Values for tree_impurity_metric
tree_impurity_gini CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_GINI';
tree_impurity_entropy CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_ENTROPY';

-- RANDOM FOREST: Setting Names
rfor_mtry CONSTANT VARCHAR2(30) := 'RFOR_MTRY';
rfor_num_trees CONSTANT VARCHAR2(30) := 'RFOR_NUM_TREES';
rfor_sampling_ratio CONSTANT VARCHAR2(30) := 'RFOR_SAMPLING_RATIO';

-- GLM: Setting Names
glms_ridge_regression CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REGRESSION';
glms_row_diagnostics CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAGNOSTICS';
glms_diagnostics_table_name CONSTANT VARCHAR2(30) := 'GLMS_DIAGNOSTICS_TABLE_NAME';
glms_reference_class_name CONSTANT VARCHAR2(30) := 'GLMS_REFERENCE_CLASS_NAME';
glms_ridge_value CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_VALUE';
glms_conf_level CONSTANT VARCHAR2(30) := 'GLMS_CONF_LEVEL';
glms_vif_for_ridge CONSTANT VARCHAR2(30) := 'GLMS_VIF_FOR_RIDGE';
glms_solver CONSTANT VARCHAR2(30) := 'GLMS_SOLVER';
glms_sparse_solver CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER';

-- GLM: Setting Values for glms_ridge_regression
glms_ridge_reg_enable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_ENABLE';
glms_ridge_reg_disable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_DISABLE';

-- GLM: Setting Values for glms_row_diagnostics
glms_row_diag_enable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_ENABLE';
glms_row_diag_disable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_DISABLE';

-- GLM: Setting Values for glms_vif_for_ridge
glms_vif_ridge_enable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_ENABLE';
glms_vif_ridge_disable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_DISABLE';

-- GLM: Setting Values for glms_ftr_selection
glms_ftr_selection CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION';
glms_ftr_selection_enable CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION_ENABLE';
glms_ftr_selection_disable CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION_DISABLE';

-- GLM: Setting Values for glms_ftr_sel_crit
glms_ftr_sel_crit CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_CRIT';
glms_ftr_sel_aic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_AIC';
glms_ftr_sel_sbic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_SBIC';
glms_ftr_sel_ric CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_RIC';
glms_ftr_sel_alpha_inv CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_ALPHA_INV';

-- GLM: Setting Values for glms_feature_generation
glms_ftr_generation CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION';
glms_ftr_generation_enable CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION_ENABLE';
glms_ftr_generation_disable CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION_DISABLE';

-- GLM: Setting Values for glms_feature_gen
glms_ftr_gen_method CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_METHOD';
glms_ftr_gen_quadratic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_QUADRATIC';
glms_ftr_gen_cubic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_CUBIC';

-- GLM: feature selection categorical value handling
glms_select_block CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK';
glms_select_block_disable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_DISABLE';
glms_select_block_enable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_ENABLE';

-- GLM: feature selection - max features selected
glms_max_features CONSTANT VARCHAR2(30) := 'GLMS_MAX_FEATURES';

-- GLM: feature identification - whether row sampling is used in the selection of feature
glms_ftr_identification CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENTIFICATION';
glms_ftr_ident_quick CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENT_QUICK';
glms_ftr_ident_complete CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENT_COMPLETE';

-- GLM: model pruning-whether the final model features will be pruned using t-statistics
glms_prune_model CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL';
glms_prune_model_enable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_ENABLE';
glms_prune_model_disable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_DISABLE';

-- GLM: feature acceptance - whether partitioning the data into feature
-- ordering and feature selection sets will be used (strict) or not (relaxed
glms_ftr_acceptance CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE';
glms_ftr_acceptance_strict CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE_STRICT';
glms_ftr_acceptance_relaxed CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE_RELAXED';

-- GLM: convergence tolerance
glms_conv_tolerance CONSTANT VARCHAR2(30) := 'GLMS_CONV_TOLERANCE';
-- GLM: number of iterations
glms_num_iterations CONSTANT VARCHAR2(30) := 'GLMS_NUM_ITERATIONS';
-- GLM: number of rows in a batch
glms_batch_rows CONSTANT VARCHAR2(30) := 'GLMS_BATCH_ROWS';

-- GLM: Setting Values for glms_solver
glms_solver_sgd CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_SGD';
glms_solver_chol CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_CHOL';
glms_solver_qr CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_QR';
glms_solver_lbfgs_admm CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_LBFGS_ADMM';

-- GLM: Setting Values for glms_sparse_solver
glms_sparse_solver_enable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_ENABLE';
glms_sparse_solver_disable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_DISABLE';

-- SVD max number of features allowed
svds_max_num_features CONSTANT NUMBER := 2500;

svds_scoring_mode CONSTANT VARCHAR2(30) := 'SVDS_SCORING_MODE';
-- SVD: Setting values for svds_scoring_mode
svds_scoring_svd CONSTANT VARCHAR2(30) := 'SVDS_SCORING_SVD';
svds_scoring_pca CONSTANT VARCHAR2(30) := 'SVDS_SCORING_PCA';
svds_u_matrix_output CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_OUTPUT';
-- SVD: Setting values for svds_u_matrix_output
svds_u_matrix_enable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_ENABLE';
svds_u_matrix_disable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_DISABLE';
-- SVD: tolerance
svds_tolerance CONSTANT VARCHAR2(30) := 'SVDS_TOLERANCE';
-- SVD: Random seed
svds_random_seed CONSTANT VARCHAR2(30) := 'SVDS_RANDOM_SEED';
-- SVD: Oversampling
svds_over_sampling CONSTANT VARCHAR2(30) := 'SVDS_OVER_SAMPLING';
-- SVD: Power iterations
svds_power_iterations CONSTANT VARCHAR2(30) := 'SVDS_POWER_ITERATIONS';
-- SVD: Solver
svds_solver CONSTANT VARCHAR2(30) := 'SVDS_SOLVER';
svds_solver_data_driven CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_DATA_DRIVEN';
svds_solver_tssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSSVD';
svds_solver_ssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_SSVD';
svds_solver_tseigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSEIGEN';
svds_solver_steigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_STEIGEN';

-- EM number of components
emcs_num_components CONSTANT VARCHAR2(30) := 'EMCS_NUM_COMPONENTS';

-- high-level component clustering
emcs_cluster_components CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMPONENTS';
-- values for emcs_cluster_components
emcs_cluster_comp_enable CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMP_ENABLE';
emcs_cluster_comp_disable CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMP_DISABLE';

-- high-level cluster threshold
emcs_cluster_thresh CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_THRESH';

-- max number of 2D attributes
emcs_max_num_attr_2d CONSTANT VARCHAR2(30) := 'EMCS_MAX_NUM_ATTR_2D';

-- number of projections
emcs_num_projections CONSTANT VARCHAR2(30) := 'EMCS_NUM_PROJECTIONS';

-- number of quantile bins
emcs_num_quantile_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_QUANTILE_BINS';

-- number of topN bins
emcs_num_topn_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_TOPN_BINS';

-- number of equi-width bins
emcs_num_equiwidth_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_EQUIWIDTH_BINS';

-- minimum percentage attribute support
emcs_min_pct_attr_support CONSTANT VARCHAR2(30) := 'EMCS_MIN_PCT_ATTR_SUPPORT';
-- full covariance (next release)
-- emcs_full_covariance CONSTANT VARCHAR2(30) := 'EMCS_FULL_COVARIANCE';
-- values for emcs_full_covariance
-- emcs_full_cov_enable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_ENABLE';
-- emcs_full_cov_disable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_DISABLE';

-- cluster statistics
emcs_cluster_statistics CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_STATISTICS';
-- values for emcs_cluster_statistics
emcs_clus_stats_enable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_ENABLE';
emcs_clus_stats_disable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_DISABLE';

-- distribution for modeling numerical attributes
emcs_num_distribution CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTRIBUTION';
-- values for emcs_num_distribution
emcs_num_distr_bernoulli CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_BERNOULLI';
emcs_num_distr_gaussian CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_GAUSSIAN';
emcs_num_distr_system CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_SYSTEM';

-- number of iterations
emcs_num_iterations CONSTANT VARCHAR2(30) := 'EMCS_NUM_ITERATIONS';

-- required log likelihood improvement
emcs_loglike_improvement CONSTANT VARCHAR2(30) := 'EMCS_LOGLIKE_IMPROVEMENT';

-- linkage function
emcs_linkage_function CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_FUNCTION';
-- values for linkage function
emcs_linkage_single CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_SINGLE';
emcs_linkage_average CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_AVERAGE';
emcs_linkage_complete CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_COMPLETE';

-- attribute filtering
emcs_attribute_filter CONSTANT VARCHAR2(30) := 'EMCS_ATTRIBUTE_FILTER';
-- values for attribute filtering
emcs_attr_filter_enable CONSTANT VARCHAR2(30) := 'EMCS_ATTR_FILTER_ENABLE';
emcs_attr_filter_disable CONSTANT VARCHAR2(30) := 'EMCS_ATTR_FILTER_DISABLE';
-- convergence criterion
emcs_convergence_criterion CONSTANT VARCHAR2(30) := 'EMCS_CONVERGENCE_CRITERION';
-- values for convergence criterion
emcs_conv_crit_heldaside CONSTANT VARCHAR2(30) := 'EMCS_CONV_CRIT_HELDASIDE';
emcs_conv_crit_bic CONSTANT VARCHAR2(30) := 'EMCS_CONV_CRIT_BIC';
-- random seed
emcs_random_seed CONSTANT VARCHAR2(30) := 'EMCS_RANDOM_SEED';

-- model search
emcs_model_search CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH';
-- values for model search
emcs_model_search_enable CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH_ENABLE';
emcs_model_search_disable CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH_DISABLE';

-- remove components
emcs_remove_components CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPONENTS';
-- values for remove components
emcs_remove_comps_enable CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPS_ENABLE';
emcs_remove_comps_disable CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPS_DISABLE';

-- ESA
esas_value_threshold CONSTANT VARCHAR2(30) := 'ESAS_VALUE_THRESHOLD';
esas_min_items CONSTANT VARCHAR2(30) := 'ESAS_MIN_ITEMS';
esas_topn_features CONSTANT VARCHAR2(30) := 'ESAS_TOPN_FEATURES';

-- ADMM
admm_iterations CONSTANT VARCHAR2(30) := 'ADMM_ITERATIONS';
admm_consensus CONSTANT VARCHAR2(30) := 'ADMM_CONSENSUS';
admm_tolerance CONSTANT VARCHAR2(30) := 'ADMM_TOLERANCE';

-- LBFGS
lbfgs_history_depth CONSTANT VARCHAR2(30) := 'LBFGS_HISTORY_DEPTH';
lbfgs_scale_hessian CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN';
lbfgs_scale_hessian_enable CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN_ENABLE';
lbfgs_scale_hessian_disable CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN_DISABLE';
lbfgs_gradient_tolerance CONSTANT VARCHAR2(30) := 'LBFGS_GRADIENT_TOLERANCE';

-- RGLU: Setting Values
ralg_build_function CONSTANT VARCHAR2(30) := 'RALG_BUILD_FUNCTION';
ralg_build_parameter CONSTANT VARCHAR2(30) := 'RALG_BUILD_PARAMETER';
ralg_score_function CONSTANT VARCHAR2(30) := 'RALG_SCORE_FUNCTION';
ralg_details_function CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FUNCTION';
ralg_details_format CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FORMAT';
ralg_weight_function CONSTANT VARCHAR2(30) := 'RALG_WEIGHT_FUNCTION';
ralg_featurematrix_function CONSTANT VARCHAR2(30) := 'RALG_FEATUREMATRIX_FUNCTION';
ralg_clustercenter_function CONSTANT VARCHAR2(30) := 'RALG_CLUSTERCENTER_FUNCTION';
r_formula CONSTANT VARCHAR2(30) := 'R_FORMULA';

-- NNET
nnet_hidden_layers CONSTANT VARCHAR2(30) := 'NNET_HIDDEN_LAYERS';
nnet_nodes_per_layer CONSTANT VARCHAR2(30) := 'NNET_NODES_PER_LAYER';
nnet_iterations CONSTANT VARCHAR2(30) := 'NNET_ITERATIONS';
nnet_tolerance CONSTANT VARCHAR2(30) := 'NNET_TOLERANCE';
nnet_activations CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS';
nnet_activations_log_sig CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_LOG_SIG';
nnet_activations_linear CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_LINEAR';
nnet_activations_tanh CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_TANH';
nnet_activations_arctan CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_ARCTAN';
nnet_activations_bipolar_sig CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_BIPOLAR_SIG';
nnet_activations_relu CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_RELU';
nnet_regularizer CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER';
nnet_regularizer_heldaside CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_HELDASIDE';
nnet_regularizer_l2 CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_L2';
nnet_regularizer_none CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_NONE';
nnet_heldaside_ratio CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_RATIO';
nnet_heldaside_max_fail CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_MAX_FAIL';
nnet_reg_lambda CONSTANT VARCHAR2(30) := 'NNET_REG_LAMBDA';
nnet_weight_lower_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_LOWER_BOUND';
nnet_weight_upper_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_UPPER_BOUND';
nnet_solver CONSTANT VARCHAR2(30) := 'NNET_SOLVER';
nnet_solver_adam CONSTANT VARCHAR2(30) := 'NNET_SOLVER_ADAM';
nnet_solver_lbfgs CONSTANT VARCHAR2(30) := 'NNET_SOLVER_LBFGS';

-- ADAM
adam_batch_rows CONSTANT VARCHAR2(30) := 'ADAM_BATCH_ROWS';
adam_alpha CONSTANT VARCHAR2(30) := 'ADAM_ALPHA';
adam_beta1 CONSTANT VARCHAR2(30) := 'ADAM_BETA1';
adam_beta2 CONSTANT VARCHAR2(30) := 'ADAM_BETA2';
adam_gradient_tolerance CONSTANT VARCHAR2(30) := 'ADAM_GRADIENT_TOLERANCE';

-- CUR approximated number of selected attributes
curs_approx_attr_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ATTR_NUM';

-- row importance
curs_row_importance CONSTANT VARCHAR2(30) := 'CURS_ROW_IMPORTANCE';

-- row importance values
curs_row_imp_enable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_ENABLE';
curs_row_imp_disable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_DISABLE';

-- approximated number of selected rows
curs_approx_row_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ROW_NUM';

-- SVD rank
curs_svd_rank CONSTANT VARCHAR2(30) := 'CURS_SVD_RANK';

-- EXSM
exsm_model CONSTANT VARCHAR2(30) := 'EXSM_MODEL';
exsm_simple CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE';
exsm_simple_mult CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE_MULT_ERR';
exsm_holt CONSTANT VARCHAR2(30) := 'EXSM_HOLT';
exsm_holt_dmp CONSTANT VARCHAR2(30) := 'EXSM_HOLT_DAMPED';
exsm_mul_trnd CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND';
exsm_multrd_dmp CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND_DAMPED';
exsm_seas_add CONSTANT VARCHAR2(30) := 'EXSM_SEASON_ADD';
exsm_seas_mul CONSTANT VARCHAR2(30) := 'EXSM_SEASON_MUL';
exsm_hw CONSTANT VARCHAR2(30) := 'EXSM_WINTERS';
exsm_hw_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_DAMPED';
exsm_hw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS';
exsm_dhw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS_DAMPED';
exsm_hwmt CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND';
exsm_hwmt_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND_DMP';
exsm_seasonality CONSTANT VARCHAR2(30) := 'EXSM_SEASONALITY';
exsm_interval CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL';
exsm_interval_year CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_YEAR';
exsm_interval_qtr CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_QTR';
exsm_interval_month CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MONTH';
exsm_interval_week CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_WEEK';
exsm_interval_day CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_DAY';
exsm_interval_hour CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_HOUR';
exsm_interval_min CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MINUTE';
exsm_interval_sec CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_SECOND';
exsm_accumulate CONSTANT VARCHAR2(30) := 'EXSM_ACCUMULATE';
exsm_accu_total CONSTANT VARCHAR2(30) := 'EXSM_ACCU_TOTAL';
exsm_accu_std CONSTANT VARCHAR2(30) := 'EXSM_ACCU_STD';
exsm_accu_max CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MAX';
exsm_accu_min CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MIN';
exsm_accu_avg CONSTANT VARCHAR2(30) := 'EXSM_ACCU_AVG';
exsm_accu_median CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MEDIAN';
exsm_accu_count CONSTANT VARCHAR2(30) := 'EXSM_ACCU_COUNT';
exsm_setmissing CONSTANT VARCHAR2(30) := 'EXSM_SETMISSING';
exsm_miss_min CONSTANT VARCHAR2(30) := 'EXSM_MISS_MIN';
exsm_miss_max CONSTANT VARCHAR2(30) := 'EXSM_MISS_MAX';
exsm_miss_avg CONSTANT VARCHAR2(30) := 'EXSM_MISS_AVG';
exsm_miss_median CONSTANT VARCHAR2(30) := 'EXSM_MISS_MEDIAN';
exsm_miss_last CONSTANT VARCHAR2(30) := 'EXSM_MISS_LAST';
exsm_miss_first CONSTANT VARCHAR2(30) := 'EXSM_MISS_FIRST';
exsm_miss_prev CONSTANT VARCHAR2(30) := 'EXSM_MISS_PREV';
exsm_miss_next CONSTANT VARCHAR2(30) := 'EXSM_MISS_NEXT';
exsm_miss_auto CONSTANT VARCHAR2(30) := 'EXSM_MISS_AUTO';
exsm_prediction_step CONSTANT VARCHAR2(30) := 'EXSM_PREDICTION_STEP';
exsm_opt_criterion CONSTANT VARCHAR2(30) := 'EXSM_OPTIMIZATION_CRIT';
exsm_opt_crit_lik CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_LIK';
exsm_opt_crit_mse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MSE';
exsm_opt_crit_amse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_AMSE';
exsm_opt_crit_sig CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_SIG';
exsm_opt_crit_mae CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MAE';
exsm_nmse CONSTANT VARCHAR2(30) := 'EXSM_NMSE';
exsm_confidence_level CONSTANT VARCHAR2(30) := 'EXSM_CONFIDENCE_LEVEL';


--MSET-SPRT
mset_memory_vectors CONSTANT VARCHAR2(30) := 'MSET_MEMORY_VECTORS';
mset_adb_height CONSTANT VARCHAR2(30) := 'MSET_ADB_HEIGHT';
mset_std_tolerance CONSTANT VARCHAR2(30) := 'MSET_STD_TOLERANCE';
mset_alpha_prob CONSTANT VARCHAR2(30) := 'MSET_ALPHA_PROB';
mset_beta_prob CONSTANT VARCHAR2(30) := 'MSET_BETA_PROB';
mset_alert_count CONSTANT VARCHAR2(30) := 'MSET_ALERT_COUNT';
mset_alert_window CONSTANT VARCHAR2(30) := 'MSET_ALERT_WINDOW';
mset_heldaside CONSTANT VARCHAR2(30) := 'MSET_HELDASIDE';
mset_projection_threshold CONSTANT VARCHAR2(30) := 'MSET_PROJECTION_THRESHOLD';

-- XGBoost
xgboost_num_round CONSTANT VARCHAR2(30) := 'num_round';
xgboost_booster CONSTANT VARCHAR2(30) := 'booster';
xgboost_objective CONSTANT VARCHAR2(30) := 'objective';
xgboost_eta CONSTANT VARCHAR2(30) := 'eta';
xgboost_gamma CONSTANT VARCHAR2(30) := 'gamma';
xgboost_max_depth CONSTANT VARCHAR2(30) := 'max_depth';
xgboost_min_child_weight CONSTANT VARCHAR2(30) := 'min_child_weight';
xgboost_max_delta_step CONSTANT VARCHAR2(30) := 'max_delta_step';
xgboost_subsample CONSTANT VARCHAR2(30) := 'subsample';
xgboost_colsample_bytree CONSTANT VARCHAR2(30) := 'colsample_bytree';
xgboost_colsample_bylevel CONSTANT VARCHAR2(30) := 'colsample_bylevel';
xgboost_lambda CONSTANT VARCHAR2(30) := 'lambda';
xgboost_alpha CONSTANT VARCHAR2(30) := 'alpha';
xgboost_tree_method CONSTANT VARCHAR2(30) := 'tree_method';
xgboost_sketch_eps CONSTANT VARCHAR2(30) := 'sketch_eps';
xgboost_scale_pos_weight CONSTANT VARCHAR2(30) := 'scale_pos_weight';
xgboost_updater CONSTANT VARCHAR2(30) := 'updater';
xgboost_grow_policy CONSTANT VARCHAR2(30) := 'grow_policy';
xgboost_max_leaves CONSTANT VARCHAR2(30) := 'max_leaves';
xgboost_max_bin CONSTANT VARCHAR2(30) := 'max_bin';
xgboost_predictor CONSTANT VARCHAR2(30) := 'predictor';
xgboost_sample_type CONSTANT VARCHAR2(30) := 'sample_type';
xgboost_normalize_type CONSTANT VARCHAR2(30) := 'normalize_type';
xgboost_rate_drop CONSTANT VARCHAR2(30) := 'rate_drop';
xgboost_one_drop CONSTANT VARCHAR2(30) := 'one_drop';
xgboost_skip_drop CONSTANT VARCHAR2(30) := 'skip_drop';
xgboost_tweedie_variance_power CONSTANT VARCHAR2(30) := 'tweedie_variance_power';
xgboost_base_score CONSTANT VARCHAR2(30) := 'base_score';
xgboost_eval_metric CONSTANT VARCHAR2(30) := 'eval_metric';
xgboost_seed CONSTANT VARCHAR2(30) := 'seed';
xgboost_ntree_limit CONSTANT VARCHAR2(30) := 'ntree_limit';
xgboost_top_k CONSTANT VARCHAR2(30) := 'top_k';
xgboost_feature_selector CONSTANT VARCHAR2(30) := 'feature_selector';
xgboost_colsample_bynode CONSTANT VARCHAR2(30) := 'colsample_bynode';
xgboost_num_parallel_tree CONSTANT VARCHAR2(30) := 'num_parallel_tree';
Data Types TYPE SETTING_LIST IS TABLE OF CLOB INDEX BY VARCHAR2(30);

SUBTYPE TRANSFORM_LIST IS dbms_data_mining_transform.TRANSFORM_LIST;
Dependencies
ALL_MINING_MODEL_SETTINGS DM_CLUSTERS DM_NESTED_NUMERICALS
ANYDATASET DM_COST_ELEMENT DM_NMF_FEATURE
DBA_MINING_MODELS DM_COST_MATRIX DM_NMF_FEATURE_SET
DBMS_ASSERT DM_EM_COMPONENT DM_QGEN
DBMS_DATA_MINING_INTERNAL DM_EM_COMPONENT_SET DM_RANKED_ATTRIBUTE
DBMS_DATA_MINING_TRANSFORM DM_EM_PROJECTION DM_RANKED_ATTRIBUTES
DBMS_DM_EXP_INTERNAL DM_EM_PROJECTION_SET DM_RULE
DBMS_DM_UTIL DM_GLM_COEFF DM_RULES
DBMS_LOB DM_GLM_COEFF_SET DM_SVD_MATRIX
DBMS_LOCK DM_ITEMS DM_SVD_MATRIX_SET
DBMS_PREDICTIVE_ANALYTICS DM_ITEMSET DM_SVM_LINEAR_COEFF
DBMS_PRIV_CAPTURE DM_ITEMSETS DM_SVM_LINEAR_COEFF_SET
DBMS_SQL DM_MODEL_GLOBAL_DETAIL DM_TRANSFORM
DBMS_STANDARD DM_MODEL_GLOBAL_DETAILS DM_TRANSFORMS
DBMS_SYS_ERROR DM_MODEL_SETTING DRVDDLR
DBMS_UTILITY DM_MODEL_SETTINGS DRVODM
DM$RQMOD_DETAILIMPL DM_MODEL_SIGNATURE ODM_MODEL_UTIL
DMP_SEC DM_MODEL_SIGNATURE_ATTRIBUTE ORA_MINING_VARCHAR2_NT
DMP_SYS DM_NB_DETAIL PLITBLM
DMUTIL_LIB DM_NB_DETAILS USER_MINING_MODELS
DM_CLUSTER DM_NESTED_NUMERICAL XMLTYPE
Documented No
First Available Not known but the creation date is January 11, 2002
Pragmas PRAGMA SUPPLEMENTAL_LOG_DATA(default, UNSUPPORTED);
Security Model Owned by SYS with EXECUTE granted to PUBLIC
Source {ORACLE_HOME}/rdbms/admin/dbmsdm.sql
{ORACLE_HOME}/rdbms/admin/prvtdm.plb
Subprograms
ADD_COST_MATRIX EXPORT_SERMODEL GET_MODEL_DETAILS_RA
ADD_PARTITION FETCH_ALG_SCHEMA GET_MODEL_DETAILS_SVD
ALTER_REVERSE_EXPRESSION GET_ASSOCIATION_RULES GET_MODEL_DETAILS_SVM
APPLY GET_DEFAULT_SETTINGS GET_MODEL_DETAILS_XML
COMPUTE_CONFUSION_MATRIX GET_FREQUENT_ITEMSETS GET_MODEL_R_FUNCTION
COMPUTE_CONFUSION_MATRIX_PART GET_MODEL_COST_MATRIX GET_MODEL_SETTINGS
COMPUTE_LIFT GET_MODEL_DETAILS_AI GET_MODEL_SIGNATURE
COMPUTE_LIFT_PART GET_MODEL_DETAILS_EM GET_MODEL_TRANSFORMATIONS
COMPUTE_ROC GET_MODEL_DETAILS_EM_COMP GET_TRANSFORM_LIST
COMPUTE_ROC_PART GET_MODEL_DETAILS_EM_PROJ IMPORT_MODEL
CREATE_MODEL GET_MODEL_DETAILS_GLM IMPORT_SERMODEL
CREATE_MODEL2 GET_MODEL_DETAILS_GLOBAL RANK_APPLY
DROP_ALGORITHM GET_MODEL_DETAILS_KM REGISTER_ALGORITHM
DROP_MODEL GET_MODEL_DETAILS_NB REMOVE_COST_MATRIX
DROP_PARTITION GET_MODEL_DETAILS_NMF RENAME_MODEL
EXPORT_MODEL GET_MODEL_DETAILS_OC  
 
ADD_COST_MATRIX
Undocumented dbms_data_mining.add_cost_matrix(
model_name              IN VARCHAR2,
cost_matrix_table_name  IN VARCHAR2,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
partition_name          IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(add_cost_matrix, AUTO_WITH_COMMIT);
TBD
 
ADD_PARTITION
Undocumented dbms_data_mining.add_partition(
model_name  IN VARCHAR2,
data_query  IN CLOB,
add_options IN VARCHAR2 DEFAULT 'ERROR');
PRAGMA SUPPLEMENTAL_LOG_DATA(add_partition, AUTO_WITH_COMMIT);
TBD
 
ALTER_REVERSE_EXPRESSION
Undocumented dbms_data_mining.alter_reverse_expression(
model_name        IN VARCHAR2,
expression        IN CLOB,
attribute_name    IN VARCHAR2 DEFAULT NULL,
attribute_subname IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(alter_reverse_expression, AUTO_WITH_COMMIT);
TBD
 
APPLY
Undocumented dbms_data_mining.apply(
model_name          IN VARCHAR2,
data_table_name     IN VARCHAR2,
case_id_column_name IN VARCHAR2,
result_table_name   IN VARCHAR2,
data_schema_name    IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(apply, AUTO_WITH_COMMIT);
TBD
 
COMPUTE_CONFUSION_MATRIX
Undocumented dbms_data_mining.compute_confusion_matrix(
accuracy                    OUT NUMBER,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
confusion_matrix_table_name IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
cost_matrix_table_name      IN  VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN  VARCHAR2 DEFAULT NULL,
score_criterion_type        IN  VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(compute_confusion_matrix, AUTO_WITH_COMMIT);
TBD
 
COMPUTE_CONFUSION_MATRIX_PART
Undocumented dbms_data_mining.compute_confusion_matrix_part(
accuracy                    OUT sys.dm_nested_numericals,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
confusion_matrix_table_name IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',,
score_partition_column_name IN  VARCHAR2 DEFAULT 'PARTITION_NAME',
cost_matrix_table_name      IN  VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN  VARCHAR2 DEFAULT NULL,
score_criterion_type        IN  VARCHAR2);
TBD
 
COMPUTE_LIFT
Undocumented dbms_data_mining.compute_lift(
apply_result_table_name     IN VARCHAR2,
target_table_name           IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
target_column_name          IN VARCHAR2,
lift_table_name             IN VARCHAR2,
positive_target_value       IN VARCHAR2,
score_column_name           IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
num_quantiles               IN NUMBER   DEFAULT 10,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
target_schema_name          IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL,
score_criterion_type        IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(compute_lift, AUTO_WITH_COMMIT);
TBD
 
COMPUTE_LIFT_PART
Undocumented dbms_data_mining.compute_lift_part(
apply_result_table_name     IN VARCHAR2,
target_table_name           IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
target_column_name          IN VARCHAR2,
lift_table_name             IN VARCHAR2,
positive_target_value       IN VARCHAR2,
score_column_name           IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN VARCHAR2 DEFAULT 'PARTITION_NAME',
num_quantiles               IN NUMBER   DEFAULT 10,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
target_schema_name          IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL,
score_criterion_type        IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(compute_lift_part, AUTO_WITH_COMMIT);
TBD
 
COMPUTE_ROC
Undocumented dbms_data_mining.compute_roc(
roc_area_under_curve        OUT NUMBER,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
roc_table_name              IN  VARCHAR2,
positive_target_value       IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(compute_roc, AUTO_WITH_COMMIT);
TBD
 
COMPUTE_ROC_PART
Undocumented dbms_data_mining.compute_roc_part(
roc_area_under_curve        OUT sys.dm_nested_numericals,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
roc_table_name              IN  VARCHAR2,
positive_target_value       IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN  VARCHAR2 DEFAULT 'PARTITION_NAME',
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL);
TBD
 
CREATE_MODEL
Undocumented dbms_data_mining.create_model(
model_name           IN VARCHAR2,
mining_function      IN VARCHAR2,
data_table_name      IN VARCHAR2,
case_id_column_name  IN VARCHAR2,
target_column_name   IN VARCHAR2 DEFAULT NULL,
settings_table_name  IN VARCHAR2 DEFAULT NULL,
data_schema_name     IN VARCHAR2 DEFAULT NULL,
settings_schema_name IN VARCHAR2 DEFAULT NULL,
xform_list           IN sys.dbms_data_mining_transform.transform_list DEFAULT NULL);
TBD
 
CREATE_MODEL2
Undocumented dbms_data_mining.create_model2(
model_name          IN VARCHAR2,
mining_function     IN VARCHAR2,
data_query          IN CLOB,
set_list            IN sys.dbms_data_mining.setting_list,
case_id_column_name IN VARCHAR2 DEFAULT NULL,
target_column_name  IN VARCHAR2 DEFAULT NULL,
xform_list          IN sys.dbms_data_mining_transform.transform_list DEFAULT NULL);
TBD
 
DROP_ALGORITHM
Undocumented dbms_data_mining.drop_algorithm(
algorithm_name IN VARCHAR2,
cascade        IN BOOLEAN DEFAULT FALSE);
TBD
 
DROP_MODEL
Undocumented dbms_data_mining.drop_model(
model_name IN VARCHAR2,
force      IN BOOLEAN DEFAULT FALSE);
TBD
 
DROP_PARTITION
Undocumented dbms_data_mining.drop_partition(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2);
PRAGMA SUPPLEMENTAL_LOG_DATA(drop_partition, AUTO_WITH_COMMIT);
TBD
 
EXPORT_MODEL
Undocumented dbms_data_mining.export_model(
filename     IN VARCHAR2,
directory    IN VARCHAR2,
model_filter IN VARCHAR2 DEFAULT NULL,
filesize     IN VARCHAR2 DEFAULT NULL,
operation    IN VARCHAR2 DEFAULT NULL,
remote_link  IN VARCHAR2 DEFAULT NULL,
jobname      IN VARCHAR2 DEFAULT NULL);
TBD
 
EXPORT_SERMODEL
Undocumented dbms_data_mining.export_sermodel(
model_data     IN OUT NOCOPY BLOB,
model_name     IN            VARCHAR2,
partition_name IN            VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(export_sermodel, AUTO);
TBD
 
FETCH_ALG_SCHEMA
Undocumented dbms_data_mining.fetch_alg_schema RETURN CLOB;
PRAGMA SUPPLEMENTAL_LOG_DATA(fetch_alg_schema, READ_ONLY);
SELECT dbms_data_mining.fetch_alg_schema
FROM dual;

FETCH_ALG_SCHEMA
--------------------------------------------------------------------------------
{
  "type": "object",
  "properties": {
    "algo_name_display": { "type" : "object",
                           "properties" : {
                              "language" : { "type" : "string",
                                             "enum" : ["English", "Spanish", "French"],
                                             "default" : "English"},
                              "name" : { "type" : "string"}
                         }
                       },
    "function_language": {"type": "string" },
    "mining_function": {
             "type" : "object",
             "properties" :
                 { "type" : "object",
                     "properties" : {
                        "mining_function_name" : { "type" : "string"},
                        "build_function": {
                           "type": "object",
                           "properties": {
                              "function_body": { "type": "CLOB" }
                            }
                          },
                         "detail_function": {
                           "type" : "array",
                           "items" : [
                              {"type": "object",
                               "properties": {
                                  "function_body": { "type": "CLOB" },
                                  "view_columns": {  "type" : "array",
                                                     "items" : {
                                                        "type" : "object",
                                                        "properties" : {
                                                           "name" : { "type" : "string"},
                                                           "type" : { "type" : "string",
                                                                      "enum" :
["VARCHAR2", "NUMBER", "DATE", "BOOLEAN"]
                                              }
                                            }
                                          }
                                        }
                                      }
                                    }
                                  ]
                                },
                               "score_function": {
                                  "type": "object",
                                  "properties": {
                                     "function_body": { "type": "CLOB" }
                                   }
                                 },
                                "weight_function": {
                                   "type": "object",
                                   "properties": {
                                      "function_body": { "type": "CLOB" }
                                    }
                                  }
                                }
                              }
         },
        "algo_setting": {
                 "type" : "array",
                 "items" : [
                     { "type" : "object",
                       "properties" : {
                          "name" : { "type" : "string"},
                          "name_display": { "type" : "object",
                                            "properties" : {
                                               "language" : { "type" : "string",
                                               "enum" : ["English", "Spanish", "French"],
                                               "default" : "English"},
                                               "name" : { "type" : "string"}}
                                          },
                          "data_type" : { "type" : "string",
                                          "enum" : ["string", "integer", "number", "boolean"]},
                          "optional": {"type" : "BOOLEAN",
                                       "default" : "FALSE"},
                          "value" : { "type" : "string"},
                          "min_value" : { "type": "object",
                                          "properties": {
                                             "min_value": {"type": "number"},
                                             "inclusive": { "type": "boolean",
                                                            "default" : TRUE},
                                           }
                                         },
                          "max_value" : {"type": "object",
                                         "properties": {
                                            "max_value": {"type": "number"},
                                            "inclusive": { "type": "boolean",
                                                           "default" : TRUE},
                                          }
                                        },
                          "categorical choices" : { "type": "array",
                                                    "items": {
                                                       "type": "string"
                                          }
                                        },
                          "description_display": { "type" : "object",
                                                   "properties" : {
                                                      "language" : { "type" : "string",
                                                                     "enum" : ["
English", "Spanish", "French"],
                                                                     "default" :
"English"},
                                                      "name" : { "type" : "string"}}
                                        }
          }
        }
      ]
    }
  }
}
 
GET_ASSOCIATION_RULES
Undocumented dbms_data_mining.get_association_rules(
model_name       IN VARCHAR2,
topn             IN NUMBER                     DEFAULT NULL,
rule_id          IN INTEGER                    DEFAULT NULL,
min_confidence   IN NUMBER                     DEFAULT NULL,
min_support      IN NUMBER                     DEFAULT NULL,
max_rule_length  IN INTEGER                    DEFAULT NULL,
min_rule_length  IN INTEGER                    DEFAULT NULL,
sort_order       IN sys.ora_mining_varchar2_nt DEFAULT NULL,
antecedent_items IN sys.dm_items               DEFAULT NULL,
consequent_items IN sys.dm_items               DEFAULT NULL,
min_lift         IN NUMBER                     DEFAULT NULL,
partition_name   IN VARCHAR2                   DEFAULT NULL)
RETURN sys.dm_rules PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_association_rules, READ_ONLY);
TBD
 
GET_DEFAULT_SETTINGS
Undocumented dbms_data_miningget_default_settings RETURN sys.dm_model_settings PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_default_settings, READ_ONLY);
SELECT * FROM TABLE(dbms_data_mining.get_default_settings);

no rows selected
 
GET_FREQUENT_ITEMSETS
Specifying topn orders by support DESC otherwise there is no ordering dbms_data_mining.get_frequent_itemsets(
model_name         IN VARCHAR2,
topn               IN NUMBER   DEFAULT NULL,
max_itemset_length IN NUMBER   DEFAULT NULL,
partition_name     IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_itemsets PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_frequent_itemsets, READ_ONLY);
TBD
 
GET_MODEL_COST_MATRIX
Undocumented dbms_data_mining.get_model_cost_matrix(
model_name     IN VARCHAR2,
matrix_type    IN VARCHAR2 DEFAULT cost_matrix_type_score,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_cost_matrix PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_cost_matrix, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_AI
Undocumented dbms_data_mining.get_model_details_ai(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_ranked_attributes PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_ai, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_EM
Undocumented dbms_data_mining.get_model_details_em(
model_name        IN VARCHAR2,
cluster_id        IN NUMBER   DEFAULT NULL,
attribute         IN VARCHAR2 DEFAULT NULL,
centroid          IN NUMBER   DEFAULT 1,
histogram         IN NUMBER   DEFAULT 1,
rules             IN NUMBER   DEFAULT 2,
attribute_subname IN VARCHAR2 DEFAULT NULL,
topn_attributes   IN NUMBER   DEFAULT NULL,
partition_name    IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_em, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_EM_COMP
Undocumented dbms_data_mining.get_model_details_em_comp(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_em_component_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_em_comp, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_EM_PROJ
Undocumented dbms_data_mining.get_model_details_em_proj(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_em_project_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_em_proj, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_GLM
Undocumented dbms_data_mining.get_model_details_glm(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_glm_coeff_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_glm, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_GLOBAL
Undocumented dbms_data_mining.get_model_details_global(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_model_global_details PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_global, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_KM
Undocumented dbms_data_mining.get_model_details_km(
model_name        IN VARCHAR2,
cluster_id        IN NUMBER   DEFAULT NULL,
attribute         IN VARCHAR2 DEFAULT NULL,
centroid          IN NUMBER   DEFAULT 1,
histogram         IN NUMBER   DEFAULT 1,
rules             IN NUMBER   DEFAULT 2,
attribute_subname IN VARCHAR2 DEFAULT NULL,
topn_attributes   IN NUMBER   DEFAULT NULL,
partition_name    IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_km, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_NB
Undocumented dbms_data_mining.get_model_details_nb(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_nb_details PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_nb, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_NMF
Undocumented dbms_data_mining.get_model_details_nmf(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_nmf_feature_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_nmf, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_OC
Undocumented dbms_data_mining.get_model_details_oc(
model_name      IN VARCHAR2,
cluster_id      IN NUMBER   DEFAULT NULL,
attribute       IN VARCHAR2 DEFAULT NULL,
centroid        IN NUMBER   DEFAULT 1,
histogram       IN NUMBER   DEFAULT 1,
rules           IN NUMBER   DEFAULT 2,
topn_attributes IN NUMBER   DEFAULT NULL,
partition_name  IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_oc, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_RA
Undocumented dbms_data_mining.get_model_details_ra(
model_name IN VARCHAR2,
par_cur    IN sys_refcursor,
out_qry    IN VARCHAR2,
view_num   IN NUMBER DEFAULT -1)
RETURN sys.anyDataSet PIPELINED USING sys.dm$rqmod_detailimpl;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_ra, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_SVD
Undocumented dbms_data_mining.get_model_details_svd(
model_name     IN VARCHAR2,
matrix_type    IN VARCHAR2 DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_svd_matrix_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_svd, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_SVM
Undocumented dbms_data_mining.get_model_details_svm(
model_name     IN VARCHAR2,
reverse_coef   IN NUMBER   DEFAULT 0,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_svm_linear_coeff_set PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_svm, READ_ONLY);
TBD
 
GET_MODEL_DETAILS_XML
XML (PMML) versions of get model details dbms_data_mining.get_model_details_xml(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.xmlType;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_details_xml, READ_ONLY);
TBD
 
GET_MODEL_R_FUNCTION
Undocumented dbms_data_mining.get_model_r_function(
model_name      IN VARCHAR2,
r_function_type IN VARCHAR2)
RETURN VARCHAR2;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_r_function, READ_ONLY);
TBD
 
GET_MODEL_SETTINGS
Undocumented dbms_data_mining.get_model_settings(model_name IN VARCHAR2)
RETURN sys.dm_model_settings PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_settings, READ_ONLY);
TBD
 
GET_MODEL_SIGNATURE
Undocumented dbms_data_mining.get_model_signature(model_name IN VARCHAR2)
RETURN sys.dm_model_signature PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_signature, READ_ONLY);
TBD
 
GET_MODEL_TRANSFORMATIONS
Undocumented dbms_data_mining.get_model_transformations(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_transforms PIPELINED;
PRAGMA SUPPLEMENTAL_LOG_DATA(get_model_transformations, READ_ONLY);
TBD
 
GET_TRANSFORM_LIST
Undocumented dbms_data_mining.get_transform_list(
xform_list   OUT NOCOPY sys.transform_list,
model_xforms IN         sys.dm_transforms);
PRAGMA SUPPLEMENTAL_LOG_DATA(get_transform_list, READ_ONLY);
TBD
 
IMPORT_MODEL
Undocumented

Overload 1		 dbms_data_mining.import_model(
filename         IN VARCHAR2,
directory        IN VARCHAR2,
model_filter     IN VARCHAR2 DEFAULT NULL,
operation        IN VARCHAR2 DEFAULT NULL,
remote_link      IN VARCHAR2 DEFAULT NULL,
jobname          IN VARCHAR2 DEFAULT NULL,
schema_remap     IN VARCHAR2 DEFAULT NULL,
tablespace_remap IN VARCHAR2 DEFAULT NULL);
TBD
Undocumented

Overload 2		 dbms_data_mining.import_model(
model_name   IN VARCHAR2,
pmmldoc      IN sys.xmltype,
strict_check IN BOOLEAN DEFAULT FALSE);
TBD
 
IMPORT_SERMODEL
Undocumented dbms_data_mining.import_sermodel(
model_data IN BLOB,
model_name IN VARCHAR2);
PRAGMA SUPPLEMENTAL_LOG_DATA(import_sermodel, AUTO_WITH_COMMIT);
TBD
 
RANK_APPLY
Undocumented dbms_data_mining.rank_apply(
apply_result_table_name     IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
score_column_name           IN VARCHAR2,
score_criterion_column_name IN VARCHAR2,
ranked_apply_table_name     IN VARCHAR2,
top_n                       IN INTEGER  DEFAULT 1,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(rank_apply, AUTO_WITH_COMMIT);
TBD
 
REGISTER_ALGORITHM
Undocumented dbms_data_mining.register_algorithm(
algorithm_name        IN VARCHAR2,
algorithm_metadata    IN CLOB,
algorithm_description IN VARCHAR2 DEFAULT NULL);
TBD
 
REMOVE_COST_MATRIX
Undocumented dbms_data_mining.remove_cost_matrix(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(remove_cost_matrix, AUTO_WITH_COMMIT);
TBD
 
RENAME_MODEL
Undocumented dbms_data_mining.rename_model(
model_name           IN VARCHAR2,
new_model_name       IN VARCHAR2,
versioned_model_name IN VARCHAR2 DEFAULT NULL);
PRAGMA SUPPLEMENTAL_LOG_DATA(rename_model, AUTO_WITH_COMMIT);
TBD

Related Topics
Built-in Functions
Built-in Packages
DBMS_DATA_MINING_INTERNAL
DBMS_DATA_MINING_TRANSFORM
DMP_SEC
DMP_SYS
ODM_MODEL_UTIL
What's New In 21c
What's New In 23c

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