General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail:
    • Journal Metrics:

Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2014 Vol.6(2): 146-149 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2014.V6.853

An improved K-Means Algorithm Based on Association Rules

Gang Liu, Shaobin Huang, Caixia Lu, and Yudan Du

Abstract—With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big limitations that the K-Means algorithm has, namely requirements that users give the anticipated variety beforehand integer K and random selection of initial variety center, proposed K-Means improved algorithm based on the association rules technology. This method proposed the concept of the smallest rule covering set .It has relieved these two big limitations of K-Means algorithm effectively. It is used for the audit monitor target discovery and extraction process in social security domain basic old-age insurance audit methods. Thus it can provide better reference value and guiding sense for auditors.

Index Terms—Clustering, text clustering, association rules, K-means algorithm, monitoring indicators.

The authors are with the College of Computer Science and Technology at Harbin Engineering University, China. (e-mail:,,,


Cite:Gang Liu, Shaobin Huang, Caixia Lu, and Yudan Du, "An improved K-Means Algorithm Based on Association Rules," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 146-149, 2014.

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