A review of the improved K-Means Clustering Algorithms for big data

This paper has a review of the improved K-Means clustering algorithms, which improve the shortcomings of the K-Means algorithm and thus provide a better and more efficient algorithm for learning big data. The goal is to cluster several large data sets, while maintaining the simplicity and efficiency of K-Means. All of these methods upgrade the standard K-Means algorithm to an efficient algorithm for big data.

Keywords:

K-Means , Clustering , Improvement of K-Means algorithm , Big data , Quality of Clustering , Execution time , initialization , classification