Inheritance diagram for kddml.Core.DataMining.Clustering.ClusteringModelManager:
Public Member Functions | |
void | addCluster (ClusterManager cluster) throws ClusteringModelException |
ComparisonMeasure | getComparisonMeasure () |
ClusterDescriptionManager | getClusterDescription () |
ClusterManager | getCluster (int identifier) throws ClusteringModelException |
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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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.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns the aggregate function used to compare two objects. This depends on the type of clustering. Cannot return null.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns the cluster description for this model. Cannor return null.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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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.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns an iterator of cluster objects.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns the number of clusters in the model.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns true if the clustering is center-based.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns true if the clustering is distribution-based.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns the maximum number of clusters that the model can contain.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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Returns the cluster containing the input instance. This depends on the type of clustering and the comparison measure used.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |
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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.
Implemented in kddml.Core.DataMining.Clustering.ClusteringModel. |