A CSA-Based Clustering Algorithm for Large Data Sets With Mixed Numeric and Categorical Values

摘要

We have presented the clonal selection algorithm to cluster large data sets. The clustering performance of the algorithm has been evaluated using a large data set. The satisfactory results have demonstrated the effectiveness of the algorithms in discovering structures in data. For clustering analysis on the large data set with mixed numeric and categorical attributes, the CSA-based algorithm not only has a high convergence speed, but also is independent on the initialization of the prototypes and can converge to the global optimum with the probability of 1. These properties are very important to the applications of data mining.