|
Oracle Data Mining Java API Reference 10g Release 1 (10.1) B12276-01 | |||||||||
| PREV NEXT | FRAMES NO FRAMES | |||||||||
ABNMiningRule represents a rule as produced from the Adaptive Bayes Network Model.ABNModelBuildState is used to specify the AdaptiveBayesNetworkModel build state.ABNModelBuildType is used to specify the AdaptiveBayesNetworkModel build type.AdaptiveBayesNetworkModel contains the metadata and rules tables from a model build.AdaptiveBayesNetworkSettings is used to specify settings for the Adaptive Bayes Network algorithm.AdaptiveBayesNetworkSettings default settings object.CostMatrix.addEntry() methodaddEntry(Category target, Object probability) method.ApplyContentItem.ItemValue to the itemset.DataUsageEntry to this data usage specification.MiningFunctionSettings.adjustAttributesType method.MiningApplyTask to perform apply mining operation on a database table.MiningApplyTask to perform apply mining operation on a database table.RecordInstance.RecordInstance.RecordInstance.RecordInstance.RecordInstance.RecordInstance.RecordInstance.RecordInstance.ApplyContentItem represents an item to be included as part of the apply output.ApplyMultipleScoringItem with nPair=1 and useTop=true. The prediction column (predAttr) must be provided, while the probability column (probAttr) is still optional. The sequence ID is included in the apply output table by default if the input data is transactional.ApplyContentOptionItem with the specified ApplyResultContentOption.ApplyMultipleScoringItem is an element to construct a MiningApplyOutput object that is used as specification of the apply output.ApplyMultipleScoringItem object with all score/probability pairs and the destination columns to appear in the apply output table.ApplyMultipleScoringItem object with the number of top n score/probability pairs and the destination columns to appear in the apply output table.ApplyMultipleScoringItem with the number of score/probability pairs to be predicted, the destination columns to appear in the output table, and the indicator as to whether to choose from the top or the bottom.ApplyContentOptionItem.ApplyRuleItem is used to construct a MiningApplyOutput object used for the AdaptiveBayesNetwork apply mining operation.ApplyRuleItem with the specified rule attribute.ApplySourceAttributeItem is used to construct a MiningApplyOutput object used for the apply mining operation.ApplySourceAttributeItem with the specified source attribute and destination attribute.ApplyTargetProbabilityItem contains a set of target values whose prediction and probability are to appear in the apply output table, regardless rank.ApplyTargetProbabilityItem.AprioriAlgorithmSettings is used to specify settings for the Apriori Algorithm.AprioriAlgorithmSettingsAssociationRulesFunctionSettings describes settings for an association rules model.AssociationRulesModel is a Java representation of the association rules model.Attribute maps to a column with a name and datatype.AttributeHistogram contains metadata used to describe one-dimensional (single attribute) histograms.AttributeHistogram object for a given attribute.AttributeImportanceAlgorithmSettings is the common superclass of all attribute importance algorithms and is used to specify parameters specific to Attribute Importance algorithms.AttributeImortanceEntry class represents an attribute with its importance found by running an attribute importance algorithm.AttributeImportanceFunctionSettings describes the settings for Attribute Importance function that captures the high-level specification for building an attribute importance model.AttributeImportanceModel is a Java representation of the attribute importance model.AttributeInstance supports providing named and typed values as input for various functions.AttributeType is used to specify whether a mining attribute is categorical or numerical.AttributeUsage is used to specify how an attribute is used for a mining operation.BooleanOperator is used to specify the boolean operator that is used in a CompoundPredicate instance.BooleanPredicate always returns the value TRUE.MiningBuildTask to build a mining model.MiningAttribute as categorical.CategoricalDiscretization allows a user to specify category groups or use automated discretization (binning) involving the N most frequent items.CategoricalDiscretization instance with the specified category groups.CategoricalDiscretization instance with topNFrequencies and the "Other" category name.Category represents a value of a categorical attribute, for example, the value "blue" of attribute "color", or number "5" of the attribute "rating".Category instance with display name set to the string representation of the boolean, true or false.Category instance with display name set to the string representation of the float.Category instance with display name set to the string representation of the int.Category instance with display name set to the string representation of the value.Category instance with display name set to the value.Category instance accepting the value as a string with the data type is used for the display name.Category instance with the provided display name.Category instance with the provided display name.Category instance with the provided display name.Category instance with the provided display name value and data type.CategoryMatrix represents a sparse square matrix whose axes are categories.CategoryMatrix.CategorySet represents a group of categories associated with a single mining attribute.CategorySet instance accepting the name and the datatype.ClassificationFunctionSettings describes the settings necessary to build a classification model.accuracy is not supported. Use other constructor without accuracy parameter.accuracy is not supported. Use other constructor without accuracy parameter.ClassificationFunctionSettings object.ClassificationFunctionSettings object.ClassificationTestResult represents the result of the test operation for a classification model.ClassificationTestTask is used for testing a model on test data.Cluster holds the metadata about a cluster in a CluteringModel.ClusterCentroid holds metadata about the centroid of a cluster.ClusterCentroidEntry contains one attribute and value pair for a cluster centroid.ClusteringAlgorithmSettings is used to specify optional parameters common to clustering algorithm.ClusteringFunctionSettings holds metadata for required settings common to all clustering algorithms.ClusteringFunctionSettings object using the default clustering MiningAlgorithmSettings.ClusteringFunctionSettings object using the clustering MiningAlgorithmSettings specified by mas.ClusteringModel holds the metadata of the result of a trained clustering model.ClusteringStoppingCriterion is used to represent the stopping criteria used by clustering models.CombinationAdaptiveBayesNetworkSettings object.CombinationNaiveBayesSettings object.ComparisonFunction is used to specify the comparison function for the Predicate interface.CompundPredicate is a set of predicates connected by logical operators.MiningLiftTask to perform the compute lift mining operation.ConditionalProbabilityExpression contains the probability of a consequent conditioned on an array of antecedent values.Connection defines methods to communicate with a Data Mining Server (DMS) in an Oracle database.CostMatrix.AttributeImportanceFunctionSettings from the specified parameters.AssociationRulesFunctionSettings from the specified parameters.ClassificationFunctionSettings from the specified parameters.RegressionFunctionSettings from the specified parameters.LogicalDataSpecification with the default settings based on the database table specified in the input.AssociationRulesFunctionSettings from the specified parameters.DataUsageSpecification instance with the specified LogicalDataSpecification, default AttributeUsage, and default DataPreparationStatus for all attributes in the LogicalDataSpecification.DataUsageSpecification instance with the specified LogicalDataSpecification, default AttributeUsage, default DataPreparationStatus, and the target attribute.create methods.DiscretizationSpecification objects for the provided attributes.DiscretizationSpecification objects for the provided attributes.create method.DataUsageSpecification instance with the specified attribute as a target attribute.CrossValidateTask provides an additional technique for measuring the accuracy of a predictive model.DataFormatType indicates whether the phisical data is specified in the transactional form.DataMiningServer is used as a proxy to create connections to a Data Mining Server.DataType is used to specify whether the data type of an attribute is integer, float, character, string, boolean or unstructured.DataUsageEntry specifies how to use a particular mining attribute in the LogicalDataSpecification of a given MiningFunctionSettingsDataUsageEntry for the mining attribute with the specified usage type.DataUsageEntry for the mining attribute with the specified usage type and preparation status.DataUsageSpecification is used to specify how the attributes in a LogicalDataSpecification instance are used for building a mining model.DataUsageSpecification instance with no data usage entries specified.DiscretizationSpecification contains discretization details for a single attribute.DiscretizationSpecification object.discretize method.DistanceFunction is used to represent the distance functions used by clustering models.error stopping criterion.errorAndIterations stopping criterion.ErrorMetric that is used by support vector machines (SVM) RegressionFunctionSettings.euclidean distance function.LocationAcessData instances are equal if all attribues are exact matches.ExecutionHandle serves as a handle for the application that can be used to monitor or cancel a task execution.FeatureExtractionAlgorithmSettings is used to specify optional parameters common to feature extraction algorithm.FeatureExtractionFunctionSettings describes settings for Feature Extraction functions.FeatureExtractionFunctionSettings object.MiningAttributes.ApplyContentOptionItem objects contained in this object.ApplyMultipleScoringItem objects contained in this object.ApplyContentItem objects contained in this object.ApplyResultContentOption.ApplyRuleItem object contained in this object.ApplySourceAttributeItem objects contained in this object.ApplyTargetProbabilityItem objects contained to this object.MiningAttribute associated with the histogram.ClusterCentroidEntry.DataUsageEntry.MiningAttribute specified by attrName, the AttributeHistogram for the cluster identified by clusterID.attrName.AttributeImportanceEntry objects in the model, each of which contains an attribute name, its importance value, and its rank.AttributeImportanceEntry objectsAttributeImportanceEntry objects based on the threshold specified for attribute importance value, given the connection to the data mining server and the model name.BooleanOperator.PhysicalDataSpecification object used to build the model.categoryGroups in the categorical discretization.Cluster objects that are children of the cluster node.TreeNodes associated with this tree node.ClassificationFunctionSettings object used to build this entry's model.ClusterCentroid object associated with a cluster.MiningRuleSet with rules representing the ClusteringModel's clusters.MiningRuleSet with rules, with at most maxAttributeRelevanceRank antecedents, representing the ClusteringModel's clusters.MiningRuleSet with rules, with at most maxAttributeRelevanceRank antecedents, representing the ClusteringModel's clusters.Vector of Cluster objects.ComparisonFunction.MiningLiftResult object for this entry's model.MiningAttribute.DataPreparationStatus enum value of the DataUsageEntry.Category.DataUsageSpecification instance.DataUsageEntry instance associated with specifed named attribute.TreeNodes forming a decision tree.NetworkFeature.accuracy is not used.Category.DistanceFunction specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.getExecutionDuration method.Vector of ClusterCentroidEntry objects.MiningAttribute.NetworkFeature if the NetworkFeature was not scored, otherwise it returns the actual scoring time.PhysicalDataSpecification object used to test and calculate lift for the model.NetworkFeature number.NetworkFeature scoring time.float, return the stored float value.ABNModelBuildState enumeration object corresponding to the specified ID.ABNModelBuildType enumeration object corresponding to the specified ID.ApplyResultContentOption enumeration that corresponds to the specified identifier.AttributeType enumeration object corresponding to the specified ID.AttributeUsage enumeration object corresponding to the specified ID.BooleanOperator enumeration object corresponding to the specified ID.ClusteringStoppingCriterion enumeration object corresponding to the specified ID.ComparisonFunction enumeration that corresponds to the specified identifier.DataFormatType enumeration object corresponding to the specified ID.DataPreparationStatus enumeration object corresponding to the specified ID.DataType enumeration object corresponding to the specified ID.DistanceFunction enumeration object corresponding to the specified ID.ErrorMetric enumeration object corresponding to the specified id.LocationEqualityLevel enumeration object corresponding to the specified ID.MiningAlgorithm enumeration object corresponding to the specified ID.MiningFunction enumeration that corresponds to the specified identifier.MiningStandardType enumeration object corresponding to the specified ID.MiningTaskState enumeration object corresponding to the specified ID.MiningTaskType enumeration that corresponds to the specified identifier.Normalization enumeration object corresponding to the specified ID.PairCombinationsOption enumeration object corresponding to the specified ID.RuleAnnotationType enumeration object corresponding to the specified ID.RuleSortCriteria enumeration that corresponds to the specified identifier.SortOrder enumeration that corresponds to the specified identifier.SparsitySpecification enumeration object corresponding to the specified ID.UsageAdjustment enumeration that corresponds to the specified identifier.ABNModelBuildState enumeration object corresponding to the specified name.ABNModelBuildType enumeration object corresponding to the specified name.ApplyResultContentOption enumeration that corresponds to the specified name.AttributeType enumeration object corresponding to the specified name.AttributeUsage enumeration object corresponding to the specified name.BooleanOperator enumeration object corresponding to the specified name.ClusteringStoppingCriterion enumeration object corresponding to the specified name.ComparisonFunction enumeration object corresponding to the specified name.DataFormatType enumeration object corresponding to the specified name.DataPreparationStatus enumeration object corresponding to the specified name.DataType enumeration object corresponding to the specified name.DistanceFunction enumeration object corresponding to the specified name.ErrorMetric enumeration object corresponding to the specified name.LocationEqualityLevel enumeration object corresponding to the specified name.MiningAlgorithm enumeration object corresponding to the specified name.MiningFunction enumeration that corresponds to the specified name.MiningStandardType enumeration object corresponding to the specified name.MiningTaskState enumeration object corresponding to the specified name.MiningTaskType enumeration that corresponds to the specified name.Normalization enumeration object corresponding to the specified name.PairCombinationsOption enumeration object corresponding to the specified name.RuleAnnotationType enumeration object corresponding to the specified name.RuleSortCriteria enumeration that corresponds to the specified name.SortOrder enumeration that corresponds to the specified name.SparsitySpecification enumeration object corresponding to the specified name.UsageAdjustment enumeration that corresponds to the specified name.int, return the stored integer value.MiningAttribute which is the subject of the comparison.ItemValues.Cluster IDs.Vector of Cluster objects.Cluster object.LocationAccessData instance identifying where the source data resides.getMaxPredictors and getNaiveBayesNumPredictors.maxNumberOfIterations specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.minimumErrorTolerance specified by a KMeansAlgorithmSettings object to train a K-Means ClusteringModel.MiningAlgorithm used to build this model.MiningAlgorithmSettings objects.Category value associated with the mining attribute.MiningFunction used to build this model.MiningFunctionSettings used to build this model.ABNModelBuildState instance indicating the model build state.ModelSeekerResultEntry objects.NetworkFeatures.ClusteringModel.Cluster.TreeNode of this tree node.Category for the positive target value.Predicate, if any, characterizing the relation between the node and its children.Predicate.PredicatesClusteringModel's root Cluster object.ABNMiningRule associated with the rule id.MiningRuleSet.MiningRuleSet that contains the specified number of rules sorted in the specified order (confidence or support).MiningRuleSet that contains the specified number of rules sorted in the specified order (confidence or support).getRules method with the following parameters.
dmsConnmodelNameselectionCriterion=RuleSortCriteria.confidenceselectionFunction=ComparisonFunction.geselectionPoint=confidence threshold valueruleOrder=nullsortOrder=nullmaxNumRulesMiningRuleSet that contains the specified items as antecedent and consequent.MiningRuleSet that contains the specified number of rules sorted in the specified order (confidence or support).MiningRuleSet associated with the array of rule ids.getRules method with the following parameters.
dmsConnmodelNameselectionCriterion=RuleSortCriteria.supportselectionFunction=ComparisonFunction.geselectionPoint=support threshold valueruleOrder=nullsortOrder=nullmaxNumRulessensitivity specified in the OCluster algorithm settings.SplitPredicate object that stores information on how records are assigned to the cluster node's children.String, return the stored String value; otherwise return null.CategorySetgetNumberOfPriorsMiningTestResult object for this entry's model.MiningTaskStatus instance.AttributeUsage enum value used for the mining attribute.Category.ClusterCentroidEntry.Category value to which the mining attribute value is compared.Object getValue(Category target) method.Category and column Category position.AttrributeInstance with the given name.NetworkFeature was accepted or rejected by the MDL pruning criteria.LocationAccessData.true if the given index value is found in the matrix.NetworkFeature met termination criteria or whether the NetworkFeature extension process was prematurely halted.ItemValue represents an item used in the Association Rules model and supports MiningRule by providing item data in an itemset.ItemValue.iterations stopping criterion.KernelFunction that is used by support vector machines (SVM) mining algorithm.KMeansAlgorithmSettings is used to specify settings for the KMeans clustering algorithm.KMeansAlgorithmSettings default settings object.KMeansAlgorithmSettings object with the maximum number of K-Means iterations between splits set to iterations, the minimum percentual change in error between K-Means iterations set to error, and the distance function to be used to train a K-Means set to distanceFunction.LiftResultElement contains information on the lift result for a specific quantile of data.ABNModelBuildState enumerations defined.ABNModelBuildType enumerations defined.ApplyResultContentOption enumeration.AttributeType enumerations defined.AttributeUsage enumerations defined.BooleanOperator enumerations defined.ClusteringStoppingCriterion enumerations defined.ComparisonFunction enumerations defined.DataFormatType enumerations defined.DataPreparationStatus enumerations defined.DataType enumerations defined.DistanceFunction enumerations defined.ErrorMetric enumerations defined.LocationEqualityLevel enumerations defined.MiningAlgorithm enumerations defined.MiningFunction enumerations.MiningStandardType enumerations defined.MiningTaskState enumerations defined.MiningTaskType enumerations defined.Normalization enumerations defined.PairCombinationsOption enumerations defined.RuleAnnotationType enumerations defined.RuleSortCriteria enumerations defined.SortOrder enumerations.SparsitySpecification enumerations defined.UsageAdjustment enumerations.listDisplayNames method.listCategories method.MiningLiftResult objects stored in the database.MiningLiftResult objects stored in the database created between the specified start and end times.MiningLiftResult objects stored in the database derived from the speicfied model, and created between the specified start and end times.ABNModelBuildState enumerations defined.ABNModelBuildType enumerations defined.ApplyResultContentOption.AttributeType enumerations defined.AttributeUsage enumerations defined.BooleanOperator enumerations defined.ClusteringStoppingCriterion enumerations defined.ComparisonFunction enumerations defined.DataFormatType enumerations defined.DataPreparationStatus enumerations defined.DataType enumerations defined.DistanceFunction enumerations defined.ErrorMetric enumerations defined..LocationEqualityLevel enumerations defined.MiningAlgorithm enumerations defined.MiningFunction.MiningStandardType enumerations defined.MiningTaskState enumerations defined.MiningTaskType enumerations defined.Normalization enumerations defined.PairCombinationsOption enumerations defined.RuleAnnotationType enumerations defined.RuleSortCriteria enumerations defined.SortOrder.SparsitySpecification enumerations defined.UsageAdjustment.listDisplayNames method.listCategories method.PriorProbabilities instance.LocationAccessData allows users to specify the location of input and output data tables.LocationAccessData for the specified location string.LocationAccessData for the specified object name and schema name.LocationCellAccessData for the specified row and column in specified schema and table.LocationEqualityLevel is used to specify the degree of equality between two LocationAccessData instances.LogicalDataSpecification (LDS) is used to describe the logical characteristics of the data used in model building, and is composed of a set of mining attributes.LogicalDataSpecification with the specified array of MiningAttributes.Connection instance.MinimumDescriptionLengthSettings is used to specify parameters for the Minimum Description Length algorithm supporting attribute importance.MiningAlgorithm specifies the algorithm used to build a mining model.MiningAlgorithmSettings is the common superclass of all mining algorithm settings.MiningApplyOutput specifies the data (columns) to be included in the apply output table created as the result of the apply mining operation.MiningApplyOutput.MiningApplyResult represents the result of the apply mining operation.MiningApplyTask is used for applying a model to a data set to make predictions, classifications, and to provide associated probabilities.MiningAttribute is a logical concept that describes a domain of data to be used as input to data mining operations.MiningAttribute instance with the specified name, data type and attribute type.MiningBuildResult represents the result of the build mining operation.MiningBuildTask is used for building all mining models supported by ODM.MiningDataTask is the common superclass for all mining tasks that involve data mining operations e.g, (building models, scoring(apply), testing, cross-validate, lift computation).MiningFunction specifies the type of mining function to be used to build a mining model.MiningFunctionSettings (MFS) captures the high level specification input for building a data mining model.MiningLiftResult contains the result of a lift computation for a classification model.MiningLiftTask is used to compute the lift based on the specified positive target value and the number of quantiles.MiningModel is the result of a successful build mining operation.MiningObjectException is thrown when an invalid mining object is detected, or when an error is detected, other than an invalid argument, that prevents successful completion of a method of a mining object.MiningOperationException is thrown when an error occurs during the execution of a mining operation.MiningRule represents a rule as produced from the Association Rules model, the Adaptive Bayes Network model, or the Clustering model.MiningRuleSet provides methods to support retrieval of rules from the Association Rules model, the Adaptive Bayes Network model, and the Clustering model.MiningTask is the common superclass for all data mining task classes.MiningTaskException is thrown when there is a failure during the execution of a mining task.MiningTaskState is used to represent the states of a mining task.MiningTaskStatus provides the following details on the state of a tasks execution: MiningTaskState enumeration State entry timestamp State description A given task may have multiple MiningTaskStatus instances that provide a status history for the task .MiningTaskStatus instance with the specified MiningTaskState, state entry time stamp, and description.MiningTaskType is used to represent the types of a mining task.MiningTestResult represents the result of mining test operation.ModelExportTask is used to export ODM mining models to standard format mining model representations.ModelExportTask object given the MiningStandardType and the location of the cell (that is, row-column intersection) identified by the column name and rowid for a given schema and table name.ModelImportTask is used to import standard format mining models to the database.ModelImportTask object given the MiningStandard and the location of the cell (that is, row-column intersection) identified by the column name and rowid for a given schema and table name.ModelSeekerClassificationAlgorithmSettings object by invoking the parent constructor with an argument value that identifies the algorithm as the Model Seeker algorithm.ModelSeekerTask with information needed to build multiple models.ModelSignature specifies the input attributes required to apply data using a specific model.NaiveBayesModel contains the metadata and Bayes statistics from the training run (see class MiningBuildTask).NaiveBayesSettings is used to specify settings for the Naive Bayes algorithm.NaiveBayesSettings instance with the default singletonThreshold value of 0.01 and pairwiseThreshold value of 0.001NaiveBayesSettings instance.NetworkFeature consists of ConditionalProbabilityExpressions.NMFAlgorithmSettings is used to specify settings for the NMF Feature Extraction algorithm.NMFAlgorithmSettings object with default settings: maximum number of iterations is set to 50 minimum convergence tolerance is set 0.5NMFAlgorithmSettings object with default settings: maximum number of iterations is set to 50NMFAlgorithmSettings object with default settings: minimum convergence tolerance is set 0.5NMFAlgorithmSettings object.NMFModel is a Java representation of the Non-negative matrix factorization(NMF) model.NonTransactionalDataSpecification instance instructs the DMS to treat associated data as "non-transactional", that is, the data consists of one record (row) per case.NonTransactionalDataSpecification(LocationAccessData)NonTransactionalDataSpecification instance with the specified location access data.Normalization indicates whether the attribute representation is zScore or minMax.MiningAttribute, such as keys, that are neither categorical or numerical.MiningAttribute as numerical, eg. containing values that are continuous or discrete numbersNumericalBin specifies explicit bin boundaries for a numerical mining attribute.NumericalBin instance that specifies the bin boudaries for a mining attribute.NumericalBin instance that specifies the bin boudaries for a mining attribute.NumericalDiscretization contains the binning details for a numerical attribute.NumericalDiscretization instance with the specified number of quantiles.NumericalDiscretization instance with the specified number of quantiles and tail percentage.NumericalDiscretization instance with the specified array of numerical bin boundaries.OClusterAlgorithmSettings holds metadata about settings that are required in the O-Cluster algorithm.OClusterAlgorithmSettings object with the the parameter sensitivity set to its default value.OClusterAlgorithmSettings object with the the parameter sensitivity set to the value of the argument.ODMException is a common superclass for ODM exceptions.ODMVersion provides the product and version information of Oracle10i Data Mining Java API.Predicate is the common superclass for SimplePredicate, BooleanPredicate, and CompoundPredicate.PredicateRuleComponent consists of a predicate - simple, boolean or compound.MinimumDescriptionLengthSettings for attribute importance instead.PriorProbabilities contains prior probabilities corresponding to target values.PriorProbabilities instance.RecordInstance represents a single record of data.RegressionFunctionSettings describes the settings necessary to build a Regression (regression) model by using a Support Vector Machine mining algorithm.RegressionFunctionSettings object.RegressionFunctionSettings object.RegressionTestResult represents the result of the test operation for a RegressionTestResult model.RegressionTestTask is used for testing an Regression (regression) model on test data.ModelSeekerResult object of the given name.AssociationRulesModel with the specified name persisted in the data mining server.AttributeImportanceModel with the specified name persisted in the data mining server given a connection to the data mining server and the model name.ClusteringModel and populates the complex model object for reading by a user program.NMFModel and populates the complex model object for reading by a user program.SupervisedModel with the specified name persisted in the data mining server given a connection to the data mining server and the model name.SupervisedModel with the specified name persisted in the data mining server given a database connection to the data mining server and the model name.ClassificationTestResult object with the specified resultName from the database.MiningLiftResult object with the specified resultName from the database.ModelSeekerResult object of the given name.MiningFunctionSettings with the specified name persisted in the data mining server given a database connection and the function settings name.MiningFunctionSettings with the specified name persisted in the data mining server given a connection to the data mining server and the function settings name.MiningLiftTask object from the database.MiningApplyOutput with the specified name persisted in the data mining server given a database connection and the name of the MiningApplyOutput.RuleAnnotation supports extensible annotations on a MiningRule.RuleAnnotation with a RuleAnnoationType and its value.RuleAnnotationType indicates an annotation type to be contained in an instance of MiningRule.RuleComponent can be used as an antecedent or consequent of a MiningRule instance.RuleComponent with a support value and an itemset.RuleSortCriteria provides options for sorting mining rules retrieved from an Association Rules model.LocationAcessData instances are equal if the schema values are exact matches.accuracy is not used.ClusteringModel.setMaxPredictors and setNaiveBayesNumPredictors.ClusteringModel.DataPreparationStatus enum value for the mining attribute.AttributeUsage enum value for the mining attribute.SignatureAttribute is used to represent a logical attribute that was used for model building and is required for scoring data using a specific model.SimplePredicate consists of a single comparison between a mining attribute value and a set of constants.SortOrder provides options for ordering of objects.SparsitySpecification indicates whether the attribute representation is dense or sparse.SupervisedFunctionSettings describes settings for supervised learning functions.accuracy is not used. Use other constructor without accuracy parameter.accuracy is not used. Use other constructor without accuracy parameter.SupervisedFunctionSettings object.SupervisedModel serves as a common superclass for supervised learning models.SupportVectorMachineModel contains the metadata and tables from a model build.SupportVectorMachineSettings is the common superclass of all Support Vector Machine (SVM) mining algorithm settings.SVMClassificationSettings is used to specify settings for the Support Vector Machine (SVM) mining algorithm to build a classification mining model.SVMClassificationSettings object with default settings: - normalization is set to minMax - kernelFunction is set to linear - tolerance float value is set to 0.001 - complexityFactor value is set to 1 - kernelCacheSize value is set to 50,000,000SVMClassificationSettings object.SVMRegressionSettings is used to specify settings for the Support Vector Machine (SVM) mining algorithm to build a regression mining model.SVMRegressionSettings object with default settings: - default normalization is set to minMax - default kernelFunction is set to linear - default tolerance value is set to 0.001 - default complexityFactor value is set to 1 - default kernelCacheSize value is set to 50,000,000 - default epsilon value is set to 0.1SVMRegressionSettings object.TargetItem represents a target value and its probability and count.TaskStatusHistory is an ordered list iterator of task execution statuses.MiningTestTask to perform the test mining operation.MiningAttribute, as an Oracle Text type.Category instance.CategoryMatrix instance.LocationAccessData object.TransactionalDataSpecification instructs the DMS to treat associated data as transactional, i.e., a given case is stored in multiple records in a table with column roles: sequenceID, attributeName, and value.TransactionalDataSpecification(seqId, attrName, value, LocationAccessData)TransactionalDataSpecification with the specified identifier attributes.Transformation is used to prepare input data for use in data mining operations in ODM.TreeNode characterizes a partition of a multidimensional dataset.UsageAdjustment specifies how the usage of a set of mining attributes is to be changed in association with Attribute Importance results.CostMatrix.validate() methodTransactionalDataSpecification.NMFAlgorithmSettings object.NaiveBayesSettings.
|
| |||||||||
| PREV NEXT | FRAMES NO FRAMES | |||||||||