Abstract
The k-prototypes algorithms are well known for their efficiency to cluster mixed numeric and categorical data. In kprototypes type algorithms the initial cluster centers are often determined in a random manner. It is acknowledged that the initial placement of cluster centers has a direct impact on the performance of the k-prototypes algorithms. However, most of the existing initialization approaches are designed for the k-means or k-modes algorithms, which can only deal with either pure numeric or categorical data, but not the mixture of both. In this paper, we propose a novel cluster center initialization method for the k-prototypes algorithms to address this issue. In the proposed method, the centrality of data objects is introduced based on the concept of neighborset, and then both the centrality and distance are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments in comparison with that of traditional random initialization method.
Original language | English |
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Pages (from-to) | 2933-2942 |
Number of pages | 10 |
Journal | Applied Mathematics & Information Sciences |
Volume | 9 |
Issue number | 6 |
Early online date | 1 Nov 2015 |
DOIs | |
Publication status | Published - 2015 |
Bibliographical note
AcknowledgementsThis work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. (21127010, 61202309), China Postdoctoral Science Foundation under Grant No. 2013M530956, Science and Technology Development Plan of Jilin province under Grant No. 20140520068JH, Fundamental Research Funds for the Central Universities under No. 14QNJJ028, the open project program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University under Grant No. 93K172014K07, the 2014 Industrial Technology Research and Development Special Project of Jilin Province, the 2015 Department of Education 12th Five-Year Science and Technology Research Planning Projects of Jilin Province. The authors are grateful to the anonymous referee for a careful checking of the details and for helpful comments that improved this paper
Keywords
- clustering
- data mining
- mixed numeric and categorical data
- cluster center initialization