A Systematic review of K-means Algorithm Improvement Research for Data Clustering

Clustering as a process to understand the nature and structure of data plays an important role in organizing data in many areas of science and technology. One of the most widely used and simple algorithms for clustering is K-means. The present study was conducted to systematically reviewing research on improving K-means algorithm on data clustering. This research examines the researches conducted in this field and its role in organizing data in the range of 2010 to 2020 with a new strategy based on the shortcomings of the K-means algorithm. For this purpose, the amount of attention of researchers to eliminate any of the shortcomings of this algorithm in order to improve it in recent years has been compiled in the form of research questions. In this study, with the use of a search strategy for refining and extracting articles, 47 related sources were identified and examined. Findings showed that most researches have been done by overcoming the sensitive shortcomings to initial cluster centers to improve the K-means algorithm. Also, out of a total of 47 studies, the improved K-means algorithm has been applied in 35 studies on non-textual data and in 12 studies on textual data. Finally, the results of a review of six studies showed that the amount of data is directly related to the performance of improved K-means algorithm. In other words, this algorithm must be modified in such a way as to perform efficient and accurate clustering by applying it to different amounts of data.