Study Of Existing Methods & Techniques Of K-Means Clustering

Abstract

In the field of data mining, clustering is the technique of grouping millions of data points to form clusters. Data of the same class are grouped together. K-Means clustering is the most important and basic clustering technique for analyzing data points. K-means is the most widely used algorithm for clustering using a known set of medians. In the past, various efforts have been made to improve the performance of the k-means algorithm. Improvements in k-means significantly improve performance for small to medium-sized data. However, for big and very large amounts of data, k-means lags. This study explores and reviews existing techniques for adapting and developing data grouping methodologies for clustering k-devices.