title: A novel artificial bee colony based clustering algorithm for categorical data.
abstract: 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.
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, China; Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
authors.forename: Jinchao
authors.initials: J
authors.lastname: Ji
authors.affiliation: School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, United Kingdom.
authors.forename: Wei
authors.initials: W
authors.lastname: Pang
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, China; Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China.
authors.forename: Yanlin
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China; College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, China; Key Lab of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun, China.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Ma
mesh_terms: D000363:Agaricales / Q000145:classification; D000465:Algorithms; D000818:Animals; D000836:Animals, Zoo / Q000145:classification*; D001943:Breast Neoplasms / Q000145:classification; D016000:Cluster Analysis; D019992:Databases as Topic; D003880:Dermatology; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification; D013025:Glycine max / Q000145:classification; D013997:Time Factors