Inheritance diagram for kddml.Core.DataMining.Clustering.SIMPLE_MATCHING:
Public Member Functions | |
SIMPLE_MATCHING (AttributeComparisonMeasure[] attribute_comparison, double[] weights) | |
SIMPLE_MATCHING (ClusterDescription cluster_description) | |
boolean | isSimilarityMeasure () |
double | compare (ClusterManager cluster, Instance instance) throws ClusteringModelException |
String | getFunctionName () |
Protected Member Functions | |
double | evaluate (double[] x) |
D = ( a11 + a00 ) / ( a11 + a10 + a01 + a00 ).
Title: KDDML
Description: Knowledge Discovery in Database Environment
Copyright: Copyright (c) 2003-2005
Company: Universita' di Pisa - Dipartimento di Informatica
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Constructor given the list of comparison measures. Used for centroid-based clustering only.
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Constructor given the centroid description. Used both for centroid-based clustering and distribution-based clustering.
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Returns true if the comparison measure is a similarity function, in which the value returned by the compare method is optimal for greater values. Returns false if the comparison measure is a distance measure, in which the value returned by the compare method is optimal if it is 0.
Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure. |
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Compares an input istance with the seed of the cluster. The seed depends on the type of clustering. If the clustering is center-based, the seed is the centroid as instance. If the clustering is distribution-based, the seed is calculated on the statistics associated to the cluster. In particular, for numeric attributes, the mean of cluster instances is used as seed. For discrete attribute, the most probable category is reported as seed.
Reimplemented from kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure. |
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Evaluates the comparison measure given the comparison values for each single attribute (i.e., the values returned by each single inner function).
Implements kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure. |
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Returns the function name as in PMML.
Implements kddml.Core.DataMining.Clustering.ComparisonMeasure. |