An Improved Genetic Clustering Algorithm for Categorical Data
Deng et al. [Deng, S., He, Z., Xu, X.: G-ANMI: A mutual information based genetic clustering algorithm for categorical data, Knowledge-Based Systems 23, 144–149(2010)] proposed a mutual information based genetic clustering algorithm named G-ANMI for categorical data. While G-ANMI is superior or comp...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Book Section |
| Language: | English |
| Published: |
Springer
2013
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6186/ http://umpir.ump.edu.my/id/eprint/6186/ http://umpir.ump.edu.my/id/eprint/6186/ http://umpir.ump.edu.my/id/eprint/6186/1/PAKDD13.pdf |
| Summary: | Deng et al. [Deng, S., He, Z., Xu, X.: G-ANMI: A mutual information based genetic clustering algorithm for categorical data, Knowledge-Based Systems 23, 144–149(2010)] proposed a mutual information based genetic clustering algorithm named G-ANMI for categorical data. While G-ANMI is superior or comparable to existing algorithms for clustering categorical data in terms of clustering accuracy, it is very time-consuming due to the low efficiency of genetic algorithm (GA). In this paper, we propose a new initialization method for G-ANMI to improve its efficiency. Experimental results show that the new method greatly improves the efficiency of G-ANMI as well as produces higher clustering accuracy. |
|---|