A Novel Intuitionistic Fuzzy Set Generator with Application to Clustering

Abstract

We often have many datasets where hard clustering algorithms do not deliver satisfactory clustering results. It is found that many times fuzzy clustering technique improves the clustering results obtained by hard clustering algorithms. Fuzzy c-means (FCM) is the most prominent fuzzy clustering techniques whose improvement was proposed through the introduction of intuitionistic fuzzy set (IFS) based c-means algorithm. In order to implement IFS based c-means algorithm over a real valued dataset, data points were first converted into IFSs by employing a highly popular technique known as Yager’s generating function. The Yager’s generating function tunes only the non-membership and hesitancy component of an IFS. Therefore, IFS based c-means algorithm produces compromised clustering results. In this paper, we have generalized the Yager’s generating function in such a manner that our IFS generation function tunes all the three components of the IFSs. We have utilized the proposed IFS generation function in two highly used IFS based c-means algorithms of clustering known as intuitionistic fuzzy c-means (IFCM) and Novel intuitionistic fuzzy c-means (Novel-IFCM) algorithms on the UCI datasets. Our results obtained using the proposed function are better than the results obtained using YGF.