A CSA-based clustering algorithm for large data sets with mixed numeric and categorical values

In the field of data mining, it is often encountered to perform cluster analysis on large data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. For this purpose, this paper presents a novel clustering algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The clonal selection algorithm (CSA) is used to optimize the new cost function. Experimental result illustrates that the CSA-based new clustering algorithm is feasible for the large data sets with mixed numeric and categorical values.