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ABNModelBuildState
is used to
specify the AdaptiveBayesNetworkModel
build state.AccessData
with specified user and password
AdaptiveBayesNetworkModel
contains the metadata and rules tables from a model build.AdaptiveBayesNetworkSettings
is used to specify settings for the Adaptive Bayes Network algorithm.ApplyContentItem
.
ItemValue
to the itemset.
DataUsageEntry
to this data usage specification.
MiningApplyTask
to perform apply
mining operation on a database table.
MiningApplyTask
to perform apply
mining operation on a database table.
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 - class oracle.dmt.odm.result.ApplyMultipleScoringItem.
- An instance of
ApplyMultipleScoringItem
is an element to construct
a MiningApplyOutput
object that is used as specification of the apply output. - ApplyMultipleScoringItem(Attribute, Attribute) -
Constructor for class oracle.dmt.odm.result.ApplyMultipleScoringItem
- Creates an instance of
ApplyMultipleScoringItem
object
with all score/probability pairs and the destination columns
to appear in the apply output table.
- ApplyMultipleScoringItem(Attribute, Attribute, int) -
Constructor for class oracle.dmt.odm.result.ApplyMultipleScoringItem
- Creates an instance of
ApplyMultipleScoringItem
object
with the number of top n score/probability pairs and the destination
columns to appear in the apply output table.
- ApplyMultipleScoringItem(Attribute, Attribute, int, boolean) -
Constructor for class oracle.dmt.odm.result.ApplyMultipleScoringItem
- Creates an instance of
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.
- ApplyResultContentOption - class oracle.dmt.odm.ApplyResultContentOption.
- Deprecated. As of ODM 9.2.0, no longer used due to deprecation of
ApplyContentOptionItem
. - ApplySourceAttributeItem - class oracle.dmt.odm.result.ApplySourceAttributeItem.
- An instance of
ApplySourceAttributeItem
is used to construct
a MiningApplyOutput
object used for the apply mining operation. - ApplySourceAttributeItem(MiningAttribute, Attribute) -
Constructor for class oracle.dmt.odm.result.ApplySourceAttributeItem
- Creates an instance of
ApplySourceAttributeItem
with the specified
source attribute and destination attribute.
- ApplyTargetProbabilityItem - class oracle.dmt.odm.result.ApplyTargetProbabilityItem.
- An instance of
ApplyTargetProbabilityItem
contains a set of
target values whose prediction and probability are to appear in the apply output table,
regardless rank. - ApplyTargetProbabilityItem(Attribute, Attribute, Attribute) -
Constructor for class oracle.dmt.odm.result.ApplyTargetProbabilityItem
- Creates an empty instance of
ApplyTargetProbabilityItem
.
- aPrioriAssociationRules -
Static variable in class oracle.dmt.odm.MiningAlgorithm
- Specifies the A Priori algorithm for Association Rules.
- ASSIGN -
Static variable in class oracle.dmt.odm.AccessData
-
- associationRules -
Static variable in class oracle.dmt.odm.MiningFunction
- Specifies the Association Rules mining function.
- AssociationRulesFunctionSettings - class oracle.dmt.odm.settings.function.AssociationRulesFunctionSettings.
- An instance of
AssociationRulesFunctionSettings
describes settings
for an association rules model. - AssociationRulesFunctionSettings(float, float, int, LogicalDataSpecification, DataUsageSpecification) -
Constructor for class oracle.dmt.odm.settings.function.AssociationRulesFunctionSettings
- Creates an instance of association rules function settings
with the specified parameters.
- AssociationRulesFunctionSettings(float, float, int, LogicalDataSpecification, DataUsageSpecification, MiningAlgorithmSettings) -
Constructor for class oracle.dmt.odm.settings.function.AssociationRulesFunctionSettings
- Deprecated. As of ODM 9.2.0. Mining algorithm settings is not required to build an
association rules model. Use other constructor.
- AssociationRulesFunctionSettings(float, float, LogicalDataSpecification, DataUsageSpecification) -
Constructor for class oracle.dmt.odm.settings.function.AssociationRulesFunctionSettings
- Creates an instance of association rules function settings
with the specified parameters.
- AssociationRulesModel - class oracle.dmt.odm.model.AssociationRulesModel.
- The abstract class
AssociationRulesModel
is a Java representation of
the association rules model. - Attribute - class oracle.dmt.odm.data.Attribute.
- An instance of
Attribute
maps to a column with a name and datatype. - Attribute(String, DataType) -
Constructor for class oracle.dmt.odm.data.Attribute
- Creates an attribute with specified name and data type.
- AttributeDiscretization - class oracle.dmt.odm.transformation.AttributeDiscretization.
- The abstruct class AttributeDiscretization specifies the discretization
values of a particular attribute.
- AttributeDiscretization() -
Constructor for class oracle.dmt.odm.transformation.AttributeDiscretization
-
- AttributeHistogram - class oracle.dmt.odm.AttributeHistogram.
- An instance of
AttributeHistogram
contains metadata used to
describe one-dimensional (single attribute) histograms. - AttributeHistogram(MiningAttribute, Vector, Vector) -
Constructor for class oracle.dmt.odm.AttributeHistogram
- Creates an
AttributeHistogram
object for a given attribute.
- AttributeHistogramEntry - class oracle.dmt.odm.AttributeHistogramEntry.
- For internal use only.
- AttributeHistogramEntry(Category, int) -
Constructor for class oracle.dmt.odm.AttributeHistogramEntry
- Creates an AttributeHistogramEntry objet with
the fields set to value in the parameter list
- attributeImportance -
Static variable in class oracle.dmt.odm.MiningFunction
- Specifies the Attribute Importance function.
- AttributeImportanceAlgorithmSettings - class oracle.dmt.odm.settings.algorithm.AttributeImportanceAlgorithmSettings.
- The abstract class
AttributeImportanceAlgorithmSettings
is the common
superclass of all attribute importance algorithms and is used to specify parameters
specific to Attribute Importance algorithms. - AttributeImportanceAlgorithmSettings() -
Constructor for class oracle.dmt.odm.settings.algorithm.AttributeImportanceAlgorithmSettings
- Creates an instance of attribute importance algorithm settings.
- AttributeImportanceEntry - class oracle.dmt.odm.model.AttributeImportanceEntry.
- An instance of
AttributeImortanceEntry
class represents an attribute
with its importance found by running an attribute importance algorithm. - AttributeImportanceEntry(String, float, int) -
Constructor for class oracle.dmt.odm.model.AttributeImportanceEntry
- Internal use only.
Creates an instance of AttributeImportanceEntry
, given the
attribute name, importance value, and rank.
- AttributeImportanceFunctionSettings - class oracle.dmt.odm.settings.function.AttributeImportanceFunctionSettings.
- An instance of
AttributeImportanceFunctionSettings
describes
the settings for Attribute Importance function that captures the high-level
specification for building an attribute importance model. - AttributeImportanceFunctionSettings(LogicalDataSpecification, DataUsageSpecification) -
Constructor for class oracle.dmt.odm.settings.function.AttributeImportanceFunctionSettings
- Creates an instance of attribute importance function settings.
- AttributeImportanceFunctionSettings(LogicalDataSpecification, DataUsageSpecification, AttributeImportanceAlgorithmSettings) -
Constructor for class oracle.dmt.odm.settings.function.AttributeImportanceFunctionSettings
- Creates an instance of attribute importance function settings.
- AttributeImportanceModel - class oracle.dmt.odm.model.AttributeImportanceModel.
- The abstract class
AttributeImportanceModel
is a Java representation of
the attribute importance model. - AttributeInstance - class oracle.dmt.odm.data.AttributeInstance.
- An instance of
AttributeInstance
supports providing named and typed values as
input for various functions. - AttributeInstance(String, float) -
Constructor for class oracle.dmt.odm.data.AttributeInstance
- Constructs a float instance with the specified name and value.
- AttributeInstance(String, int) -
Constructor for class oracle.dmt.odm.data.AttributeInstance
- Constructs an integer instance with the specified name and value.
- AttributeInstance(String, String) -
Constructor for class oracle.dmt.odm.data.AttributeInstance
- Constructs a string instance with the specified name and value.
- AttributeType - class oracle.dmt.odm.AttributeType.
- The enumeration class
AttributeType
is used to
specify whether a mining attribute is categorical or numerical. - AttributeUsage - class oracle.dmt.odm.AttributeUsage.
- The enumeration class
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 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
.
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 following stopping criteria used by clustering models:CombinationAdaptiveBayesNetworkSettings
is used to
specify multiple settings for the Adaptive Bayes Network algorithm
to be used by Model Seeker.CombinationAdaptiveBayesNetworkSettings
object.
CombinationModelSettings
is the common super class
for the algorithm settings that specify
model combinations of a single type.CombinationModelSettings
are supported:CombinationNaiveBayesSettings
andCombinationAdaptiveBayesNetworkSettings
.CombinationNaiveBayesSettings
is used to specify
multiple settings for the Naive Bayes algorithm
to be used by Model Seeker.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 9i database.AttributeImportanceFunctionSettings
from the specified parameters.
ClassificationFunctionSettings
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.
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.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, or string.DataUsageEntry
specifies how to use
a particular mining attribute in the LogicalDataSpecification
of a given MiningFunctionSettings
DataUsageEntry
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.
discretize
method.
DistanceFunction
is used to represent the following distance functions used by clustering models:error
stopping criterion.
errorAndIterations
stopping criterion.
euclidean
distance function.
LocationAcessData
instances are equal if all attribues are exact matches.
MiningAttribute
s.
ApplyContentOptionItem
objects
contained in this object.
ApplyMultipleScoringItem
objects
contained in this object.
ApplyContentItem
objects contained in this object.
ApplyResultContentOption
.
ApplySourceAttributeItem
objects
contained in this object.
ApplyTargetProbabilityItem
objects
contained to this object.
MiningAttribute
associated with the histogram.
DataUsageEntry
.
ClusterCentroidEntry
.
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
objects
AttributeImportanceEntry
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.
TreeNode
s 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 maxNumberRuleAttribute
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.
TreeNode
s forming a decision tree.
accuracy
is not used.
Category
.
DistanceFunction
specified by a
KMeansAlgorithmSettings
object to train a K-Means
ClusteringModel
.
getExecutionDuration
method.
Vector
of ClusterCentroidEntry
objects.
MiningAttribute
.
PhysicalDataSpecification
object used to
test and calculate lift for the model.
float
, return the stored float value.
DataType
instance.
MiningFunction
enumeration
that corresponds to the specified identifier.
UsageAdjustment
enumeration
that corresponds to the specified identifier.
ClusteringStoppingCriterion
instance specified by id
.
AttributeUsage
enumeration object corresponding to the
specified ID.
ComparisonFunction
enumeration
that corresponds to the specified identifier.
ApplyResultContentOption
enumeration
that corresponds to the specified identifier.
RuleAnnotationType
enumeration
that corresponds to the specified identifier.
AttributeType
enumeration object corresponding to the
specified ID.
DataType
enumeration object corresponding to the
specified ID.
ABNModelBuildState
enumeration object corresponding to the
specified ID.
LocationEqualityLevel
enumeration object corresponding to the
specified ID.
BooleanOperator
enumeration object corresponding to the
specified ID.
DistanceFunction
instance specified by id
.
RuleSortCriteria
enumeration
that corresponds to the specified identifier.
DataType
instance.
MiningFunction
enumeration
that corresponds to the specified name.
UsageAdjustment
enumeration
that corresponds to the specified name.
ClusteringStoppingCriterion
instance specified by name
.
AttributeUsage
enumeration object corresponding to the
specified name.
ComparisonFunction
enumeration
that corresponds to the specified name.
ApplyResultContentOption
enumeration
that corresponds to the specified name.
RuleAnnotationType
enumeration
that corresponds to the specified name.
AttributeType
enumeration object corresponding to the
specified name.
DataType
enumeration object corresponding to the
specified name.
ABNModelBuildState
enumeration object corresponding to the
specified name.
LocationEqualityLevel
enumeration object corresponding to the
specified name.
BooleanOperator
enumeration object corresponding to the
specified name.
DistanceFunction
instance specified by name
.
RuleSortCriteria
enumeration
that corresponds to the specified name.
int
, return the stored integer value.
AttributeUsage
.
MiningAttribute
which is the subject of the comparison.
ItemValue
s.
Cluster
IDs.
Vector
of Cluster
objects.
Cluster
object.
LocationAccessData
instance identifying where
the source data resides.
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.
ModelTimingRecord
s.
NetworkFeature
s.
ClusteringModel
.
Cluster
.
TreeNode
of this tree node.
Category
for the positive target value.
Predicate
.
Predicate
, if any, characterizing the relation between the node and its children.
Predicate
s
ClusteringModel
's root
Cluster
object.
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 whose confidence is greater than the specified value.
MiningRuleSet
that contains the specified
items as antecedent and consequent.
MiningRuleSet
that contains the specified
number of mining rules whose support is greater than the specified value.
sensitivity
specified in the OCluster algorithm settings.
SplitPredicate
object that stores information on
how records are assigned to the cluster node's children.
clusteringStoppingCriterion
specified by a
KMeansAlgorithmSettings
object to train a K-Means
ClusteringModel
.
String
, return the stored String value; otherwise return null.
getNumberOfPriors
MiningTestResult
object for this entry's model.
MiningTaskStatus
instance.
AttributeUsage
enum value used for the
mining attribute.
Category
.
Category
value to which the mining attribute value is compared.
ClusterCentroidEntry
.
Category
and
column Category
position.
AttrributeInstance
with the given name.
LocationAccessData
.
ItemValue
represents an item used in the
Association Rules model and supports
MiningRule
by providing item data in an itemset.ItemValue
.
iterations
stopping criterion.
KMeansAlgorithmSettings
is used to specify settings
for the KMeans clustering algorithm.KMeansAlgorithmSettings
object with
the minimum percentual change in error between K-Means iterations
to considered that K-Means has converged set to minErrorTolerance
and the distance function to be used
to train a K-Means set to distanceFunction
minErrorTolerance
is a number between 0 and 1.
KMeansAlgorithmSettings
object with
the maximum number of K-Means iterations between splits set to
iterations
and the distance function to be used
to train a K-Means set to distanceFunction
.
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.MiningTaskState
values
MiningFunction
enumerations.
UsageAdjustment
enumerations.
ClusteringStoppingCriterion
enumerations.
AttributeUsage
enumerations defined.
ComparisonFunction
enumerations.
ApplyResultContentOption
enumeration.
RuleAnnotationType
enumerations.
AttributeType
enumerations defined.
DataType
enumerations defined.
ABNModelBuildState
enumerations defined.
LocationEqualityLevel
enumerations defined.
BooleanOperator
enumerations defined.
DistanceFunction
enumerations.
RuleSortCriteria
enumerations.
listDisplayNames
method.
listCategories
method.
MiningLiftResult
objects stored in the DMS.
MiningLiftResult
objects stored in the DMS
created between the specified start and end times.
MiningLiftResult
objects stored in the DMS
derived from the speicfied model, and created between the specified start and
end times.
MiningTaskState
names.
MiningFunction
.
UsageAdjustment
.
ClusteringStoppingCriterion
names.
AttributeUsage
enumerations defined.
ComparisonFunction
.
ApplyResultContentOption
.
RuleAnnotationType
.
AttributeType
enumerations defined.
DataType
enumerations defined.
ABNModelBuildState
enumerations defined.
LocationEqualityLevel
enumerations defined.
BooleanOperator
enumerations defined.
DistanceFunction
names.
RuleSortCriteria
.
listDisplayNames
method.
listCategories
method.
PriorProbabilities
instance.
Location
for the specified location
string.
Location
for the specified object name and schema name.
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
MiningAttribute
s.
Connection
instance.
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.
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.MiningOperationException
is thrown when an
error occurs during the execution of a mining operation.MiningResult
is a common superclass for ODM
result objects of mining operations
(build, apply, test and compute lift).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 following states of a mining task:
ready - ready to execute, a task will come to this state only
after storing the task in the DMS
queued - in this state task is in the queue of the DMS
initiated - task is dequeued and initiated to execute in the DMS
executing - task is getting excuted in the DMS
terminating - task is in the process of terminating
terminated - task is terminated
success - if the task execution succeeds
error - if the task execution fails.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.
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 DMS.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
is used to
specify multiple algorithm settings to be used by Model Seeker.ModelSeekerClassificationAlgorithmSettings
object by
invoking the parent constructor with an argument value that
identifies the algorithm as the Model Seeker algorithm.
ModelSeekerResult
contains the results objects created by the execution
of a ModelSeekerTask
.ModelSeekerResultEntry
objects,
one for each model built by the ModelSeekerTask
execution.ModelSeekerResultEntry
represents the results for a single model built by the execution
of a ModelSeekerTask
.MiningTestResult
,
MiningLiftResult
, MiningModel
, etc.) associated with
the particular model.ModelSeekerTask
is a task used to invoke the Model Seeker functionality.ModelSeekerTask
with information
needed to build multiple models.
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.
NetworkFeature
consists of ConditionalProbabilityExpression
s.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.
MiningAttribute
, such as keys, that are neither categorical or
numerical.
MiningAttribute
as numerical, eg.
NumericalBin
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
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.oracle.dmt.odm.ODMVersion
.
ODMVersion
provides the product and version
information of Oracle9i Data Mining Java API.ODMVersion
.
Predicate
is the common superclass for
SimplePredicate
, BooleanPredicate
, and CompoundPredicate
.PredicateRuleComponent
consists of a predicate - simple, boolean or compound.PredictorVarianceSettings
is used to specify
parameters for the Predictor Variance algorithm supporting attribute importance.PriorProbabilities
contains prior probabilities
corresponding to target values.PriorProbabilities
instance.
RecordInstance
represents a single record of data.ModelSeekerResult
object of the given name.
resultName
from the DMS.
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.
ModelSeekerResult
object of the given name.
MiningLiftResult
object with the
specified resultName
from the DMS.
ClusteringModel
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.
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.
MiningLiftTask
object from
the DMS.
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
.
AttributeUsage
.
ClusteringModel
.
DataPreparationStatus
enum value for the
mining attribute.
StopCriterion
to be used to train a K-Means
ClusteringModel
.
AttributeUsage
enum value for the
mining attribute.
SimplePredicate
consists of a single comparison between
a mining attribute value and a set of constants.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.MiningTestTask
to perform
the test mining operation.
CategoryMatrix
instance.
LocationAccessData
object.
Category
instance.
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.TransactionalDataSpecification
.
NaiveBayesSettings
.
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