Research Article
A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
@ARTICLE{10.4108/eai.13-7-2018.159622, author={J. Arora and M. Tushir}, title={A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={7}, number={24}, publisher={EAI}, journal_a={SIS}, year={2019}, month={7}, keywords={Semi-Supervised Clustering, Intuitionistic Fuzzy-Set, Image Segmentation}, doi={10.4108/eai.13-7-2018.159622} }- J. Arora
M. Tushir
Year: 2019
A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
SIS
EAI
DOI: 10.4108/eai.13-7-2018.159622
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.
Copyright © 2019 J. Arora et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
