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

since the clonal operator can combine the evolutionary search and random search

and incorporate the global search with local search

by the clonal operation on candidate solutions;the new algorithm can quickly obtain the global optimum.Experimental result illustrates that the CSA-based new clustering algorithm is feasible for the large data sets with mixed numeric and categorical values.