A Survey Paper on Improvisation of K-Means Clustering Algorithm with Implementation on E-Commerce Data

Clustering is a technique for primary data analysis and k-means is clustering algorithms which are very useful of all the other algorithms. In data mining clustering methods are frequently used, because its performance in clustering large data sets. The result of the k-means clustering algorithm depends upon the correctness of the initial centroids, because they are selected randomly. The original k-means algorithm converges to local minimum, not the global optimum. Many improvements were already proposed to improve the performance of the k-means, but most of these require additional inputs like threshold values for the number of data points in a set. In this research a new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. Our experimental results show the proposed algorithm more accurate with less computational time comparing to original k-means clustering algorithm.