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2007
Springer

Performance Assessment of Some Clustering Algorithms Based on a Fuzzy Granulation-Degranulation Criterion

11 years 7 months ago
Performance Assessment of Some Clustering Algorithms Based on a Fuzzy Granulation-Degranulation Criterion
In this paper a fuzzy quantization dequantization criterion is used to propose an evaluation technique to determine the appropriate clustering algorithm suitable for a particular data set. In general, the goodness of a partitioning is measured by computing the variances within it, which is a measure of compactness of the obtained partitioning. Here a new kind of error function, which reflects how well the formed cluster centers represent the whole data set, is used as the goodness of the obtained partitioning. Thus a clustering algorithm, providing a good set of centers which approximate the whole data set perfectly, is best suitable for partitioning that particular data set. Five well-known clustering algorithms, GAK-means (genetic algorithm based Kmeans algorithm), a newly developed genetic point symmetry based clustering technique (GAPS-clustering), Average Linkage clustering algorithm, Expectation Maximization (EM) clustering algorithm and Self Organizing Map (SOM) are used as th...
Sriparna Saha, Sanghamitra Bandyopadhyay
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where CIT
Authors Sriparna Saha, Sanghamitra Bandyopadhyay
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