oracle.dmt.jdm.algorithm.kmeans
Class OraKMeansSettings

java.lang.Object
oracle.dmt.jdm.OraDMObject
oracle.dmt.jdm.base.OraAlgorithmSettings
oracle.dmt.jdm.clustering.OraClusteringAlgorithmSettings
oracle.dmt.jdm.algorithm.kmeans.OraKMeansSettings

All Implemented Interfaces:

javax.datamining.base.AlgorithmSettings, javax.datamining.clustering.ClusteringAlgorithmSettings, javax.datamining.algorithm.kmeans.KMeansSettings, oracle.dmt.jdm.OraPLSQLConstants

public class OraKMeansSettings

extends oracle.dmt.jdm.clustering.OraClusteringAlgorithmSettings

implements javax.datamining.algorithm.kmeans.KMeansSettings

OraKMeansSettings is an extension of javax.datamining.algorithm.kmeans.KMeansSettings. It defines set and get of split criterion that can be specified for kMeans algorithm.

Field Summary

Fields inherited from interface oracle.dmt.jdm.OraPLSQLConstants

abns_max_build_minutes, abns_max_nb_predictors, abns_max_predictors, abns_model_type, abns_multi_feature, abns_naive_bayes, abns_single_feature, algo_adaptive_bayes_network, algo_ai_mdl, algo_ai_mdl2, algo_apriori_association_rules, algo_decision_tree, algo_kmeans, algo_naive_bayes, algo_name, algo_nonnegative_matrix_factor, algo_ocluster, algo_predictor_variance, algo_support_vector_machines, apply_cost_content, apply_nodeid_content, apply_pred_value_content, apply_probability_content, asso_max_rule_length, asso_min_confidence, asso_min_support, association, association_in_model, attribute_importance, clas_cost_table_name, clas_priors_table_name, classification, clus_num_clusters, clustering, feat_num_features, feature_extraction, kmns_block_growth, kmns_conv_tolerance, kmns_cosine, kmns_distance, kmns_euclidean, kmns_fast_cosine, kmns_iterations, kmns_min_pct_attr_support, kmns_num_bins, kmns_size, kmns_split_criterion, kmns_variance, nabs_pairwise_threshold, nabs_singleton_threshold, nmfs_conv_tolerance, nmfs_num_iterations, nmfs_random_seed, ocluster_max_buffer, ocluster_sensitivity, operator_equal, operator_equal_v, operator_greater_or_equal, operator_greater_or_equal_v, operator_greater_than, operator_greater_than_v, operator_in, operator_in_v, operator_less_or_equal, operator_less_or_equal_v, operator_less_than, operator_less_than_v, operator_not_equal, operator_not_equal_v, operator_not_in, operator_not_in_v, oracle_char_type, oracle_dm_nested_categoricals, oracle_dm_nested_numericals, oracle_float_type, oracle_number_type, oracle_varchar2_type, regression, svms_active_learning, svms_al_disable, svms_al_enable, svms_complexity_factor, svms_conv_tolerance, svms_epsilon, svms_gaussian, svms_kernel_cache_size, svms_kernel_function, svms_linear, svms_outlier_rate, svms_std_dev, tree_impurity_entropy, tree_impurity_gini, tree_impurity_metric, tree_impurity_metric_default, tree_term_max_depth, tree_term_max_depth_default, tree_term_max_depth_max, tree_term_max_depth_min, tree_term_max_surrogates_max, tree_term_max_surrogates_min, tree_term_minpct_node, tree_term_minpct_node_default, tree_term_minpct_node_max, tree_term_minpct_split, tree_term_minpct_split_default, tree_term_minpct_split_max, tree_term_minrec_node, tree_term_minrec_node_default, tree_term_minrec_split, tree_term_minrec_split_default

Method Summary

int	getBlockGrowth() Returns the growth factor for memory allocated to hold cluster data.
double	getMinPercentageAtrrSupport() Returns the minimum percentage support required for attributes in rules.
int	getNumberOfBins() Returns the number of histogram bins.
OraSplitCriterion	getSplitCriterion() Returns the Split criterion for k-Means Clustering
void	setBlockGrowth(int blockGrowth) Sets the growth factor for memory allocated to hold cluster data.
void	setMinPercentageAtrrSupport(double minPercentageAtrrSupport) Sets the minimum percentage support required for attributes in rules.
void	setNumberOfBins(int numberOfBins) Sets the number of histogram bins.
void	setSplitCriterion(OraSplitCriterion splitCriterion) Sets the Split criterion for k-Means Clustering.

Methods inherited from class oracle.dmt.jdm.base.OraAlgorithmSettings

getMiningAlgorithm, verify

Methods inherited from class oracle.dmt.jdm.OraDMObject

createException, createException, createRuntimeException, createRuntimeException, getLocalizedMessage, isConnectionOpen, logInfo, logSevere, logTrace, logTrace, unsupported, unsupported

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface javax.datamining.base.AlgorithmSettings

getMiningAlgorithm, verify

Method Detail

setSplitCriterion

public void setSplitCriterion(OraSplitCriterion splitCriterion)

Sets the Split criterion for k-Means Clustering. Default is kMeans variance.

Returns:

void

getSplitCriterion

public OraSplitCriterion getSplitCriterion()
                                    throws javax.datamining.JDMException

Returns the Split criterion for k-Means Clustering

Returns:

OraSplitCriterion

Throws:

javax.datamining.JDMException

setNumberOfBins

public void setNumberOfBins(int numberOfBins)

Sets the number of histogram bins. Specifies the number of bins in the attribute histogram produced by k-Means. Default value is 10.

Returns:

void

getNumberOfBins

public int getNumberOfBins()

Returns the number of histogram bins. Specifies the number of bins in the attribute histogram produced by k-Means.

Returns:

int

setBlockGrowth

public void setBlockGrowth(int blockGrowth)

Sets the growth factor for memory allocated to hold cluster data. Valid range > 1 and <= 5. Default value is 2.

Returns:

void

getBlockGrowth

public int getBlockGrowth()

Returns the growth factor for memory allocated to hold cluster data.

Returns:

int

setMinPercentageAtrrSupport

public void setMinPercentageAtrrSupport(double minPercentageAtrrSupport)

Sets the minimum percentage support required for attributes in rules. Specifies the minimum percentage of values for an attribute in a given cluster required to include this attribute in the rule description of the cluster. Default value is 0.1

Returns:

void

getMinPercentageAtrrSupport

public double getMinPercentageAtrrSupport()

Returns the minimum percentage support required for attributes in rules.

Returns:

double