Cuckoo-search based Intuitionistic Fuzzy Clustering Algorithm

Clustering plays an important role in the fields like medical diagnosis, pattern recognition and decision support systems. Fuzzy clustering allows an object to be a part of various clusters with various membership values. Intuitionistic fuzzy set helps in making useful decisions by adding another parameter called hesitancy which contributes to the uncertainties present in the data. This work focuses on the effective usage of Intuitionistic fuzzy clustering algorithm by hybridizing it with the most famous optimization algorithm called Cuckoo Search. The results are compared to Fuzzy C Means and Intuitionistic Fuzzy C Means algorithms. The resulting clusters are found to be of more efficient in terms of intra cluster distances minimized to a great extent and thus leading to an effective diagnosis.