标题：A Variant of Genetic Algorithm Based Categorical Data Clustering for Compact Clusters and an Experimental Study on Soybean Data for Local and Global Optimal Solutions
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：2
DOI：10.14569/IJACSA.2016.070256
出版社：Science and Information Society (SAI)
摘要：Almost all partitioning clustering algorithms getting stuck to the local optimal solutions. Using Genetic algorithms (GA) the results can be find globally optimal. This piece of work offers and investigates a new variant of the Genetic algorithm (GA) based k-Modes clustering algorithm for categorical data. A statistical analysis have been done on the popular categorical dataset which shows the user specified cluster centres stuck at local optimal solution in K-modes algorithm even in all the higher iterations and the proposed algorithm overcome this problem of local optima. To the best of our knowledge, such comparison has been reported here for the first time for the case of categorical data. The obtained results, shows that the proposed algorithm is better over the conventional k-Modes algorithm in terms of optimal solutions and within cluster variation measure.
