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...

• Type: Conference
• Year: 2007
• Where: CIT
• Authors: Sriparna Saha, Sanghamitra Bandyopadhyay
• Keywords: CIT 2007, Clustering Algorithm, Data Set, Information Technology, Particular Data Set