A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering

Arora, J. and Tushir, M. (2020) A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering. EAI Endorsed Transactions on Scalable Information Systems, 7 (24): e1. ISSN 2032-9407

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Abstract

Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data. Also the intuitionistic fuzzy sets, a generalization of fuzzy sets, have been proven to deal better with the problem of uncertainty present in the data. In this paper, we have proposed to embed the concept of intuitionistic fuzzy set theory with semi-supervised approach to further improve the clustering process. We evaluated the performance of the proposed methodology on several benchmark real data sets based on several internal and external indices. The proposed Semi-Supervised Intuitionistic Fuzzy C-means clustering is compared with several state of the art clustering/classification algorithms. Experimental results show that our proposed algorithm is a better alternative to these competing approaches.

Item Type: Article
Uncontrolled Keywords: Semi-Supervised Clustering, Intuitionistic Fuzzy-Set, Image Segmentation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:52
Last Modified: 08 Oct 2020 13:52
URI: https://eprints.eudl.eu/id/eprint/681

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