An Optimized Algorithm For Efficient Problem Solving In K-MEANS Clustering

Abstract

K-means clustering is used to cluster numerical data. In K-means we define two measures of distances, between two data points (records) and the distance between two clusters. Distance can be measured (calculated) in a number of ways but four principles tend to hold true. This paper proposes an optimized algorithm for k-means clustering. We introduce genetic algorithm skilfully, on data sets to get an improvised and better results on the data over the existing ones. This algorithm overcomes the disadvantages of the already existing algorithms for obtaining efficient results. We experimentally demonstrate that our algorithm works correctly and can optimize data sets critically for better performance.