Abstract
Clustering is an important unsupervised learning algorithm that groups records according to their similarity. However, since uncertainty has become an inherent of real world datasets, crisp clustering leads to inefficiency. Hence, introduction of uncertainty based models like the fuzzy set and the intuitionistic fuzzy set is necessary to compensate for the ambiguity in data. Huang, in his fuzzy k-modes algorithm, introduced the fuzzy component in clustering categorical data by modifying the existing k-means algorithm. This correspondence describes an intuitionistic fuzzy k-modes algorithm for clustering categorical data and establishes it to be more efficient than the fuzzy k-modes algorithm. Metrics like accuracy, DB index and Dunn index are used to compare the efficiency of the two algorithms. The experimental analysis section shows that the proposed algorithm is more efficient than the existing one. Several graphical and tabular representations have been provided for easy comparison of the results.
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References
Michalski, R.S.: Soybean (Small) Data Set. UCI Machine Learning Repository, Irvine. https://archive.ics.uci.edu/ml/datasets/Soybean+(Small)
Forsyth, R.: Zoo Data Set. UCI Machine Learning Repository, Irvine. https://archive.ics.uci.edu/ml/datasets/Zoo
Huang, Z., Ng, M.K.: A fuzzy k-modes algorithm for clustering categorical data. IEEE Trans. Fuzzy Syst. 7(4), 446–452 (1999)
Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)
Senthamilarasu, S., Hemalatha, M.: A genetic algorithm based intuitionistic fuzzification technique for attribute selection. Indian J. Sci. Technol. 6(4), 4336–4346 (2013)
Yager, R.R.: Some aspects of intuitionistic fuzzy sets. Fuzzy Optim. Decis. Mak. 8(1), 67–90 (2009)
Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: Gupta, M.M., Saridis, G.N., Gaines, B.R. (eds.) Fuzzy Automata and Decision Processes, pp. 89–102 (1977)
Bock, H.H.: Clustering methods: a history of k-means algorithms. In: Selected Contributions in Data Analysis and Classification, pp. 161–172. Springer, Berlin (2007)
Chaira, T., Anand, S.: A novel intuitionistic fuzzy approach for tumor/hemorrhage detection in medical images. J. Sci. Ind. Res. 70(6), 427–434 (2011)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–57 (1973)
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Mehta, D., Tripathy, B.K. (2017). Clustering of Categorical Data Using Intuitionistic Fuzzy k-modes. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_24
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DOI: https://doi.org/10.1007/978-981-10-3322-3_24
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