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.

Clustering is a process of organizing objects into groups whose elements are similar to each other. A cluster is therefore a collection of objects which are similar to each other and dissimilar to the objects belonging to other clusters. The goal of clustering is to quickly access interconnected information and identify logical relationships between them. Clustering algorithms can be applied in many fields. As clustering results would be affected by the number of terms used, various experimental methods are used to find the approximate number of terms expected to produce a balanced distribution of data among clusters, and set upper and lower boundaries for the clustering algorithm. One of the applications of clustering is the organization of thesauri. The main objective of this paper is to have an accurate and appropriate explanation of the information clustering concept and its application in thesaurus structure. This research utilized library study to explain effective information clustering methods and their application in thesaurus structure. Some of the main methods of clustering are cliques clustering, single link, star and string clustering. The findings show that utilization of appropriate algorithms and useful methods of information clustering play an important role in optimum thesaurus organization.