In data mining, clustering is a method of grouping similar points together. This grouping can be done using partitioning or hierarchical clustering algorithms. K-means is one of the partitioning clustering algorithms which is simple and faster than other clustering algorithms. Major drawbacks of K-means algorithm are the selection of initial centroids and number of clusters K. This paper aims at providing a solution for selecting initial centroids in which a new point is calculated at each iteration and the point in the dataset which is closest to the calculated point is selected as the centroid. The performance of the proposed work is compared with existing methods using four datasets collected from UCI repository. From the results, it is proved that the proposed work increases accuracy by 88.74% for Iris dataset, 28.18% for Breast cancer dataset, 34.03% for Seeds dataset and 18.18% for PIMA I Diabetes dataset over the other methods.
['S. Poomagal', 'P. Saranya', 'S. Karthik']
A novel method for selecting initial centroids in K-means clustering algorithm