This study proposed three meta-heuristic based fuzzy K-modes algorithms for clustering binary data set. There are three metaheuristic methods applied, namely Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA) algorithm, and Artificial Bee Colony (ABC) algorithm. These three algorithms are combined with k-modes algorithm. Their aim is to give better initial modes for the k-modes. Herein, the similarity between two instances is calculated using jaccard coefficient. The Jaccard coefficient is applied since the data set contains many zero values. In order to cluster a real data set about automobile suppliers in Taiwan, the proposed algorithms are verified using benchmark data set. The experiments results show that PSO K-modes and GA K-modes is better than ABC K-modes. Moreover, from case study results, GA fuzzy K-modes gives the smallest SSE and also has faster computational time than PSO fuzzy K-modes and ABC fuzzy K-modes.

BEZDEK, J. C. 1976. Feature selection for binary data: medical diagnosis with fuzzy sets. Proceedings of the June 7-10, 1976, national computer conference and exposition. New York, New York: ACM.
BOTTANI, E. & RIZZI, A. 2008. An adapted multi-criteria approach to suppliers and products selection—An application oriented to lead-time reduction. International Journal of Production Economics, 111, 763-781.
GAN, G., MA, C. & WU, J. 2007. Data clustering: theory, algorithms, and applications, SIAM: Society for Industrial and Applied Mathematics.
GAN, G., WU, J. & YANG, Z. 2009. A genetic fuzzy -Modes algorithm for clustering categorical data. Expert Systems with Applications, 36, 1615-1620.
HAN, J. & KAMBER, M. 2007. Data Mining : Concepts and Techniques, San Fransisco, Elsevier Inc.
HUANG, Z. 1998. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 2, 283-304.
KARABOGA, D. & AKAY, B. 2011. A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Applied Soft Computing, 11, 3021-3031.
KARABOGA, D. & BASTURK, B. 2007. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing. Cancun, Mexico: Springer-Verlag.
KENNEDY, J. & EBERHART, R. 1995. Particle swarm optimization. Neural Networks, Proceedings., IEEE International Conference on.
KENNEDY, J., KENNEDY, J. F., EBERHART, R. C. & SHI, Y. 2001. Swarm Intelligence, Morgan Kaufmann Publishers.
LEGENDRE, L. & LEGENDRE, P. 1983. Numerical Ecology, New York, Elsevier Scientific.
MEHDIZADEH, E. & TAVAKKOLI-MOGHADDAM, R. A hybrid fuzzy clustering PSO algorithm for a clustering supplier problem. Industrial Engineering and Engineering Management, 2007 IEEE International Conference on, 2-4 Dec. 2007 2007. 1466-1470.
RYDER, R. & FEARNE, A. 2003. Procurement best practice in the food industry: supplier clustering as a source of strategic competitive advantage. Supply Chain Management: An International Journal, 8, 12-16.
SALSKI, A. 2006. Ecological Applications of Fuzzy Logic. In: RECKNAGEL, F. (ed.) Ecological Informatics. Springer Berlin Heidelberg.
SHI, Y. & EBERHART, R. 1998. Parameter selection in particle swarm optimization. In: PORTO, V. W., SARAVANAN, N., WAAGEN, D. & EIBEN, A. E. (eds.) Evolutionary Programming VII. Springer Berlin Heidelberg.
SOLIMAN, O. S., SALEH, D. A. & RASHWAN, S. A hybrid fuzzy particle swarm and fuzzy k-modes clustering algorithm. Informatics and Systems (INFOS), 2012 8th International Conference on, 14-16 May 2012 2012. BIO-68-BIO-75.
TALBI, E.-G. 2000. Metaheuristic : From Design to Implement, New Jersey, John Wiley & Sons, Inc.
TAN, P.-N., STEINBACH, M. & KUMAR, V. 2006. Introduction to Data Mining, Boston, Pearson Education, Inc.
WEI & CHANG, X. 1999. A neuro-fuzzy controller for a stoker-fired boiler, based on behavior modeling. Control Engineering Practice, 7, 469-481.
YINGJUN, Z., PEIJUN, M. & XIAOHONG, S. Pattern recognition using interval-valued intuitionistic fuzzy set and its similarity degree. Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on, 20-22 Nov. 2009 2009. 361-365.
ZADEH, L. A. 1965. Fuzzy sets. Information and Control, 8, 338-353.
ZHEXUE, H. & NG, M. K. 1999. A fuzzy k-modes algorithm for clustering categorical data. Fuzzy Systems, IEEE Transactions on, 7, 446-452.