Clustering scientific articles based on the k_means algorithmCase Study: Iranian Research Institute for information Science and Technology (IranDoc)

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.