¹Department of Computer Science and Technology, Henan Radio & Television University, Zhengzhou 450008, China
²School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu
³School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

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Abstract—Similarity and inclusion measures between type-2 fuzzy sets have a wide range of applications. New similarity and inclusion measures between type-2 fuzzy sets are respectively defined in this paper. The properties of the measures are discussed. Some examples are used to compare the proposed measures with the existing results. Numerical results show that the proposed measures are more reasonable. Similarity measures and Yang and Shih’s algorithm are combined as a clustering method for type-2 fuzzy data. Clustering results demonstrate that the proposed similarity measures are better than the existing measures.

Index Terms—Type-2 fuzzy sets, similarity, inclusion, clustering.

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Cite: Dongmei Li, Tao Li, and Tao Zhao, "A New Clustering Method Based On Type-2 Fuzzy Similarity and Inclusion Measures," Journal of Computers vol. 9, no. 11, pp. 2559-2569, 2014.