In this paper a fuzzy quantization dequantization crite- rion is used to propose an evaluation technique to determine the appropriate clustering algorithm suitable for a partic- ular 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 clus- tering algorithm, providing a good set of centers which ap- proximate the whole data set perfectly, is best suitable for partitioning that particular data set. Five well-known clus- tering algorithms, GAK-means (genetic algorithm based K- means algorithm), a newly developed genetic point sym- metry based clustering technique (GAPS-clustering), Aver- age Linkage clustering algorithm, Expectation Maximiza- tion (EM) clustering algorithm and Self Organizing Map (SOM) are used as the underlying partitioning techniques. Five artificially generated and three real-life data sets are used to establish that the proposed methodology is able to correctly identify appropriate clustering algorithm for a particular data set. Keywords: Unsupervised classification, clustering algo- rithms, algorithm identification, fuzzy vector quantization.