Inheritance diagram for kddml.Core.DataMining.Clustering.ClusteringModel:
Public Member Functions | |
KDDMLObjectType | getType () |
boolean | isEmpty () |
void | saveToRepository () throws KDDMLCoreException |
String | toString () |
void | saveHTML () throws KDDMLCoreException |
void | addCluster (ClusterManager cluster) throws ClusteringModelException |
ComparisonMeasure | getComparisonMeasure () |
ClusterDescriptionManager | getClusterDescription () |
ClusterManager | getCluster (int identifier) throws ClusteringModelException |
java.util.Iterator | getClusters () |
int | getNumberOfClusters () |
boolean | isCentroidBased () |
boolean | isDistributionBased () |
int | getMaxNumberOfClusters () |
ClusterManager | getCluster (Object instance) throws ClusteringModelException |
Object | toInstances () throws ClusteringModelException |
Title: KDDML
Description: Knowledge Discovery in Database Environment
Copyright: Copyright (c) 2003-2005
Company: Universita' di Pisa - Dipartimento di Informatica
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It returns the type of the object.
Implements kddml.Core.KDDMLObject. |
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Returns ture if the model do not contain clusters.
Implements kddml.Core.KDDMLObject. |
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Save the object in the system repository. The destination path is provided by the object_path variable.
Implements kddml.Core.KDDMLObject. |
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Returns a representation of this object as string.
Reimplemented from kddml.Core.DataMining.MiningModel. |
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Save the object in the system repository as HTML document.
Implements kddml.Core.HTMLTranslator. |
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Adds a new cluster to the model, both for centroid-based and distribution-based clustering. Throws an exception if the number of clusters exceededs the maximum number of clusters allowed.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the aggregate function used to compare two objects. This depends on the type of clustering. Cannot return null.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the cluster description for this model. Cannor return null.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the Cluster object in the model with the specified identifier. Throws an exception if the specified index do not exist in the clustering model.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns an iterator of cluster objects.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the number of clusters in the model.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns true if the clustering is center-based.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns true if the clustering is distribution-based.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the maximum number of clusters that the model can contain.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns the cluster containing the input instance. This depends on the type of clustering and the comparison measure used.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |
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Returns a representation of each single cluster as instance. This depends on the type of clustering. For center-based clustering, each cluster is represented by the centroid. In this case, it returns the centroid poind as instance. For distribution-based clustering, each cluster is represented by the statistics. In this case, the instace values depend on the type of attribute. For numeric attribute, the mean containing in the statistics is reported. For discrete attribute, the most probable category value is reported.
Implements kddml.Core.DataMining.Clustering.ClusteringModelManager. |