A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numerical and Categorical Values
Li Jie Gao Xin-bo Jiao Li-cheng (School of Electronic Engineering, Xidian Univ., Xi'an 710071, China)
In the field of data mining, it is often encountered to perform cluster analysis on large data sets with mixed numerical and categorical values. However, most existing clustering algorithms are only efficient for the numerical 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 Genetic Algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. Experimental result illustrates that the GA-based new clustering algorithm is feasible for the large data sets with mixed numerical and categorical values.