A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms

Clustering is essentially a procedure of grouping a set of objects in such a manner that items within the same clusters are more akin to each other compared with those data point or objects in different amassments or clusters. This paper discusses partition-predicated clustering techniques, such as K-Means, K-Means++ and object predicated Fuzzy C-Means clustering algorithm. This paper proposes a method for getting better clustering results by application of sorted and unsorted data into the algorithms. Elapsed time & total number of iterations are the factors on which, the behavioral patterns are analyzed. The experimental results shows that passing the sorted data instead of unsorted data not only effects the time complexity but withal ameliorates performance of these clustering techniques.