k-means algorithm

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.

With the increasing growth of Web-based resources and articles, the use of quick and inexpensive ways to access the texts is important from the vast collection of these documents. The main objective of this research is to cluster the base of Iranian Research Institute for information Science and Technology (IranDoc) based on text mining techniques. So that the articles are ivided into several clusters so that the articles of the different clusters have the maximum possible difference and the articles in each cluster have the most similarity. Articles on information technology related fields were selected. For this purpose, first all the keywords of information technology fields were selected based on their frequencies in base articles and then the articles of each keyword were extracted from the Iran Doc database. Then, using the notepad ++ software, the dataset was created. In this research, clustering of k_means algorithm and Euclidean distance function criterion were used to measure the similarity of clusters. Then the results of the clustering were analyzed to find the similarity and pattern among the papers. The pattern showed that the greatest similarity is found between articles in the two data mining clusters and the neural network with an Euclidean distance of 1.365, and the least similarity between the two cluster articles is optimization and image processing with a distance of 1.387. Research knowledge, clustering of articles related to the highest and the least degree of similarity with each other, finding a new pattern for quick and easy access to similar articles, and discovering hidden relationships among different subjects. This knowledge helps researchers to access topic-related articles related to specialization Identify themselves and the subject of the study in a more desirable way.