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

Jie Li; Xinbo Gao; Licheng Jiao

doi:10.1117/12.538864

25 September 2003 A GA-based clustering algorithm for large data sets with mixed numeric and categorical values

Jie Li, Xinbo Gao, Licheng Jiao

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.538864
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

Abstract

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 exciting 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 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 numeric and categorical values.

Citation Download Citation

Jie Li, Xinbo Gao, and Licheng Jiao "A GA-based clustering algorithm for large data sets with mixed numeric and categorical values", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.538864

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