Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms

الملخص EN

Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data.

It finds interesting and promising applications in different domains.

Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures.

IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree.

The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM).

The clustering algorithm has membership and nonmembership degrees as intervals.

Information regarding membership and typicality degrees of samples to all clusters is given by algorithm.

The experiments are performed on both real and simulated datasets.

It generates valuable information and produces overlapped clusters with different membership degrees.

It takes into account inherent uncertainty in information captured by IFSs.

Some advantages of algorithms are simplicity, flexibility, and low computational complexity.

The algorithm is evaluated through cluster validity measures.

The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns.

The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.