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See:
Description
Class Summary |
Class | Description |
---|---|
AssociationRulesFunctionSettings | An instance of AssociationRulesFunctionSettings describes settings
for an association rules model. |
AttributeImportanceFunctionSettings | An instance of AttributeImportanceFunctionSettings describes
the settings for Attribute Importance function that captures the high-level
specification for building an attribute importance model. |
ClassificationFunctionSettings | An instance of ClassificationFunctionSettings describes
the settings necessary to build a classification model. |
ClusteringFunctionSettings | An instance of ClusteringFunctionSettings holds metadata for required
settings common to all clustering algorithms. |
DataUsageEntry | An instance of DataUsageEntry specifies how to use
a particular mining attribute in the LogicalDataSpecification
of a given MiningFunctionSettings |
DataUsageSpecification | An instance of DataUsageSpecification is used to specify
how the attributes in a LogicalDataSpecification
instance are used for building a mining model. |
MiningFunctionSettings | The abstract class MiningFunctionSettings (MFS) captures the high level
specification input for building a data mining model. |
SupervisedFunctionSettings | SupervisedFunctionSettings describes settings for supervised learning functions. |
This package contains Java classes representing mining function settings.
Mining function settings are named objects that application programmers
construct and provide to Oracle9i Data Mining to build a model.
It consists of a LogicalDataSpecification
and maps the physical
input data to the logical form understood by ODM,
DataUsageSpecification
that specifies how each mining attribute
is to be used for the build operation, the type of function
to be executed, and an optional algorithm settings.
An instance of mining function settings must be stored in the DMS before
it can be used to build a model.
A mining function settings object captures the high level specification input for building a data mining model. In ODM, the functions are divided into the key areas: classification, approximation, association rules, and clustering. The intent of function settings is to allow a user to specify the type of result desired without having to specify a particular algorithm. Although mining function settings allow for the specification of algorithm, if this is omitted, the underlying DMS is responsible for selecting the algorithm based on basic user-provided parameters.
ODM supports the following types of mining function settings:
AssociationRulesFunctionSettings
AttributeImportanceFunctionSettings
ClusteringFunctionSettings
SupervisedFunctionSettings
: A place holder for all supervised function settings
ClassificationFunctionSettings
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