New clustering method of mixed-attribute data
Chunguang Zhou · Journal of Jilin University · 2013
A new Global k-Prototype(GKP) algorithm is proposed for clustering mixed numeric and categorical data.First,the algorithm randomly selects a sufficiently large number of initial prototypes to account for the global distribution of the data sets.Then,it progressively eliminates the redundant prototypes using an iterative optimization process with an elimination criterion function.Systematic experiments were carried out with data from widely used datasets in this area.Experimental results and comparative evaluation show the high performance and consistency of the proposed algorithm.Compared with other well-known mixed data clustering algorithms,the proposed algorithm significantly improves the clustering accuracy.