Clustering Scientific Articles based on the K_means Algorithm Case Study: Iranian Research Institute for Information Science and Technology (IranDoc)

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SOLEIMANI NEZHAD ADEL; SALAJEGHEH MOZHDEH; Tayyebi Nia Elham

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Paper Information

Journal: Iranian Journal of Information Processing & Management Year:2019 | Volume:34 | Issue:2 Page(s): 871-896

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Title

Clustering Scientific Articles based on the K_means Algorithm Case Study: Iranian Research Institute for Information Science and Technology (IranDoc)

Author(s)

SOLEIMANI NEZHAD ADEL | SALAJEGHEH MOZHDEH | Tayyebi Nia Elham | Issue Writer Certificate

Keywords

Text MiningQ1

ClusteringQ1

K_means AlgorithmQ1

Euclidean Distance Function CriterionQ1

IranDoc DatabaseQ1

Abstract

With increasing growth of Web-based resources and articles, the use of quick and inexpensive ways to access the texts from the vast collection of these documents is important. The main objective of this research is to cluster the database of Iranian Research Institute for Information Science and Technology (IranDoc) based on Text Mining techniques, so that the articles are divided into several clusters and different clusters have maximum possible difference and the articles in each cluster have the most similarity. Articles on information technologyrelated fields were selected. For this purpose, all the keywords of information technology fields were selected first based on their frequencies in database articles and then the articles of each keyword were extracted from the IranDoc Database. Then, using 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 two data mining clusters and neural network with an Euclidean distance of 1. 365, and the least similarity between two cluster articles is optimization and image processing with a distance of 1. 387. Knowledge from this research is to: Clustering the articles related to the highest and the least degree of similarity to each other, find a new pattern for quick and easy access to similar articles, and discover hidden relationships between different topics. This knowledge helps researchers to better identify the subject-related articles related to their subject matter, which are similar to the subject matter studied.

Cites

Topic Analysis of Published Articles in Medical librarianship and Information Science in Iran Using Text Mining Techniques

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APA: Copy

SOLEIMANI NEZHAD, ADEL, SALAJEGHEH, MOZHDEH, & Tayyebi Nia, Elham. (2019). Clustering Scientific Articles based on the K_means Algorithm Case Study: Iranian Research Institute for Information Science and Technology (IranDoc). IRANIAN JOURNAL OF INFORMATION PROCESSING & MANAGEMENT (INFORMATION SCIENCES AND TECHNOLOGY), 34(2 ), 871-896. SID. https://sid.ir/paper/407906/en

Vancouver: Copy

SOLEIMANI NEZHAD ADEL, SALAJEGHEH MOZHDEH, Tayyebi Nia Elham. Clustering Scientific Articles based on the K_means Algorithm Case Study: Iranian Research Institute for Information Science and Technology (IranDoc). IRANIAN JOURNAL OF INFORMATION PROCESSING & MANAGEMENT (INFORMATION SCIENCES AND TECHNOLOGY)[Internet]. 2019;34(2 ):871-896. Available from: https://sid.ir/paper/407906/en

IEEE: Copy

ADEL SOLEIMANI NEZHAD, MOZHDEH SALAJEGHEH, and Elham Tayyebi Nia, “Clustering Scientific Articles based on the K_means Algorithm Case Study: Iranian Research Institute for Information Science and Technology (IranDoc),” IRANIAN JOURNAL OF INFORMATION PROCESSING & MANAGEMENT (INFORMATION SCIENCES AND TECHNOLOGY), vol. 34, no. 2 , pp. 871–896, 2019, [Online]. Available: https://sid.ir/paper/407906/en

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