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kddml.Core.DataMining.Clustering.ComparisonMeasure Class Reference

Inheritance diagram for kddml.Core.DataMining.Clustering.ComparisonMeasure:

kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure kddml.Core.DataMining.Clustering.DistributionBasedComparisonMeasure kddml.Core.DataMining.Clustering.BINARY_SIMILARITY kddml.Core.DataMining.Clustering.CHEBYCHEV kddml.Core.DataMining.Clustering.CITY_BLOCK kddml.Core.DataMining.Clustering.EUCLIDEAN kddml.Core.DataMining.Clustering.JACCARD kddml.Core.DataMining.Clustering.MINKOWSKI kddml.Core.DataMining.Clustering.SIMPLE_MATCHING kddml.Core.DataMining.Clustering.SQUARED_EUCLIDEAN kddml.Core.DataMining.Clustering.TANIMOTO kddml.Core.DataMining.Clustering.EM_DISTANCE List of all members.

Public Member Functions

String toString ()
abstract boolean isCentroidBased ()
abstract String getFunctionName ()

Protected Member Functions

abstract Element toXML ()

Detailed Description

When two records are compared then either the distance or the similarity is of interest. In both cases the measures can be computed by a combination of an 'inner' function and an 'outer' function. The inner function compares two single field values and the outer function computes an aggregation over all fields.
The ComparisonMeasure describes the aggregation function to be used to determine the similarity between two cases (outer comparison function). Depending on the attribute kind, the aggregated value is optimal if it is 0 (for distance measure) or greater values indicate optimal fit (for similarity measure). Comparison measures are allowed both for centroid-based clustering and disctribution-based clustering.

Title: KDDML

Description: Knowledge Discovery in Database Environment

Copyright: Copyright (c) 2003-2005

Company: Universita' di Pisa - Dipartimento di Informatica

Author:
Andrea Romei (romei@di.unipi.it)
Version:
2.0.16


Member Function Documentation

String kddml.Core.DataMining.Clustering.ComparisonMeasure.toString  ) 
 

Returns a representation of this function as string.

Returns:
String

abstract boolean kddml.Core.DataMining.Clustering.ComparisonMeasure.isCentroidBased  )  [pure virtual]
 

Returns true if the function is related to a centroid-based clustering. Returns true if the function is related to a distribution-based clustering.

Returns:
boolean

Implemented in kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure, and kddml.Core.DataMining.Clustering.DistributionBasedComparisonMeasure.

abstract Element kddml.Core.DataMining.Clustering.ComparisonMeasure.toXML  )  [protected, pure virtual]
 

Returns a representation of this function as PMML element.

Returns:
Element

Implemented in kddml.Core.DataMining.Clustering.CentroidBasedComparisonMeasure, and kddml.Core.DataMining.Clustering.EM_DISTANCE.

abstract String kddml.Core.DataMining.Clustering.ComparisonMeasure.getFunctionName  )  [pure virtual]
 

Returns the function name as used in PMML.

Returns:
String

Implemented in kddml.Core.DataMining.Clustering.BINARY_SIMILARITY, kddml.Core.DataMining.Clustering.CHEBYCHEV, kddml.Core.DataMining.Clustering.CITY_BLOCK, kddml.Core.DataMining.Clustering.EM_DISTANCE, kddml.Core.DataMining.Clustering.EUCLIDEAN, kddml.Core.DataMining.Clustering.JACCARD, kddml.Core.DataMining.Clustering.MINKOWSKI, kddml.Core.DataMining.Clustering.SIMPLE_MATCHING, kddml.Core.DataMining.Clustering.SQUARED_EUCLIDEAN, and kddml.Core.DataMining.Clustering.TANIMOTO.


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