A New Clustering Method Based On Type-2 Fuzzy Similarity and Inclusion Measures
نویسندگان
چکیده
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.
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ورودعنوان ژورنال:
- JCP
دوره 9 شماره
صفحات -
تاریخ انتشار 2014