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See:
Description
Class Summary |
Class | Description |
---|---|
AdaptiveBayesNetworkSettings | An instance of AdaptiveBayesNetworkSettings is used to specify settings for the Adaptive Bayes Network 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. |
ClusteringAlgorithmSettings | An instance of ClusteringAlgorithmSettings is used to
specify optional parameters common to clustering algorithm. |
CombinationAdaptiveBayesNetworkSettings | An instance of CombinationAdaptiveBayesNetworkSettings is used to
specify multiple settings for the Adaptive Bayes Network algorithm
to be used by Model Seeker. |
CombinationModelSettings | The abstract class CombinationModelSettings
is the common super class
for the algorithm settings that specify
model combinations of a single type.The following sub-classes of CombinationModelSettings are supported:CombinationNaiveBayesSettings andCombinationAdaptiveBayesNetworkSettings . |
CombinationNaiveBayesSettings | An instance of CombinationNaiveBayesSettings is used to specify
multiple settings for the Naive Bayes algorithm
to be used by Model Seeker. |
KMeansAlgorithmSettings | An instance of KMeansAlgorithmSettings is used to specify settings
for the KMeans clustering algorithm. |
MiningAlgorithmSettings | The abstract class MiningAlgorithmSettings is the common superclass
of all mining algorithm settings. |
ModelSeekerClassificationAlgorithmSettings | An instance of ModelSeekerClassificationAlgorithmSettings is used to
specify multiple algorithm settings to be used by Model Seeker. |
NaiveBayesSettings | An instance of NaiveBayesSettings is used to specify settings
for the Naive Bayes algorithm. |
OClusterAlgorithmSettings | An instance of OClusterAlgorithmSettings holds metadata
about settings that are required in the O-Cluster algorithm. |
PredictorVarianceSettings | An instance of PredictorVarianceSettings is used to specify
parameters for the Predictor Variance algorithm supporting attribute importance. |
This package contains Java classes representing mining algorithm settings. An algorithm settings object captures the parameters associated with a particular algorithm. It allows a knowledgeable user to fine tune algorithm parameters. Generally, not all parameters must be specified, however, those specified are taken into account by the DMS. Separating algorithm settings from function settings provides a natural and convenient separation for those users experienced with data mining and those only familiar with mining functions.
ODM supports the following types of algorithm settings:
AdaptiveBayesNetworkSettings
: used in conjunction with
ClassificationFunctionSettings
to build a
AdaptiveBayesNetworkModel
AttributeImportanceAlgorithmSettings
: An abstract class for all
attribute importance settings.
PredictorVarianceSettings
: used in conjunction with
AttributeImportanceFunctionSettings
to build a
AttributeImportanceModel
NaiveBayesSettings
: used in conjunction with
ClassificationFunctionSettings
to build a
NaiveBayesModel
ClusteringAlgorithmSettings
: An abstract class for all clustering
algorithm settings.
KMeansAlgorithmSettings
: k-means clustering algorithm
OClusteringAlgorithmSettings
: A proprietary clsutering algorithm
of Oracle9i Data Mining
NaiveBayesModel
or
AdaptiveBayesNetworkModel
)
CombinationalAlgorithmSettings
: An abstract class that contains
a compact representation of combinations of parameters for a specific
algorithm settings.
CombinationalAdaptiveBayesNetworkSettings
: Used to build
a set of AdaptiveBayesNetworkModel
via model seeker task.
CombinationalNaiveBayesSettings
: Used to build a set of
NaiveBayesModel
via model seeker task.
ModelSeekerClassificationAlgorithmSettings
: A container class
for multiple instances of classification algorithm settings of one kind.
It may contain either NaiveBayesSettings
or
AdaptiveBayesNetworkSettings
.
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