吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (01): 130-134.

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New clustering method of mixed-attribute data

BAI Tian1,2, JI Jin-chao1, HE Jia-liang1, ZHOU Chun-guang1   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130022, China;
    2. Department of Computer Science, Rutgers, the State University of New Jersey, New Jersey, NJ 08901, USA
  • Received:2011-12-28 Online:2013-01-01 Published:2013-01-01

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Abstract: 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.

Key words: artificial intelligence, clustering, data mining, K-prototypes algorithm, mixed attribute data

CLC Number: 

  • TP181
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