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A Novel Cluster Center Initialization Method for the k-Prototypes Algorithms using Centrality and Distance |
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PP: 2933-2942 |
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Author(s) |
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Jinchao Ji,
Wei Pang,
Yanlin Zheng,
Zhe Wang,
Zhiqiang Ma,
Libiao Zhang,
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Abstract |
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| 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. |
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