A novel artificial bee colony based clustering algorithm for categorical data

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

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.
Original languageEnglish
Article numbere0127125
Number of pages17
JournalPLoS ONE
Issue number5
Publication statusPublished - 20 May 2015


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