ENTROPY-BASED INTUITIONISTIC FUZZY C-MEANS CLUSTERING

Hung Quoc Truong, Cuong Anh Nguyen, Dzung Dinh Nguyen

Abstract


With the rapid development of the uncertain or hesitant and fuzziness datasets, an entropy-based intuitionistic fuzzy c-means clustering (EIFCM) method is proposed based on the intuitionistic fuzzy sets (IFS)for the clustering problems. Utilizing the advantages of the intuitionistic fuzzy sets and fuzzy sets, which are combined in the proposed method, to overcome some drawbacks of the conventional FCM in handling uncertainties or hesitant and also resolve the fuzziness. Experimental results show that the proposed algorithm is better than the traditional fuzzy clustering algorithms.

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References


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