An initialization method for clustering mixed numeric and categorical data based on the density and distance

Jinchao Ji, Wei Pang, Yanlin Zheng, Zhe Wang, Zhiqiang Ma

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.
Original languageEnglish
Article number1550024
Number of pages16
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume29
Issue number7
Early online date28 Jul 2015
DOIs
Publication statusPublished - Nov 2015

Keywords

  • clustering
  • data mining
  • mixed numeric and categorical data
  • cluster center initialization

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