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http://dx.doi.org/10.6109/jkiice.2020.24.7.942

Context-awareness User Analysis based on Clustering Algorithm  

Lee, Kang-whan (Department of Computer Science Engineering, ICT Convergence, Interdisciplinary Program in Creative Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.7, 2020 , pp. 942-948 More about this Journal
Abstract
In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.
Keywords
Clustering Algorithm; K-means Clustering; non-supervisor learning; Context-awareness;
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1 G. Adami, P. Avesani, and D. Sona, "Clustering documents in a web directory," Proceedings of the 5th ACM international workshop on Web information and data management, vol.54, Issue 3, pp.66-73, Sep. 2015.
2 E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long. "A survey of clustering with deep learning: From the perspective of network architecture," IEEE Access, vol.6, 39501-39514, July 2018.   DOI
3 J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A K-Means Clustering Algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100-108, Jan. 2012.
4 A. Likas and N. Vlassis and J.Verbeek, "The global k-means clustering algorithm," Department of Computer Science, University of Ioannina, 45110 Joannina, Greece, vol. 36, no.2, pp 451-461, Feb. 2018.   DOI
5 L. Xue and W. Luan, "Improved K-means Algorithm in User Behavior Analysis," Ninth International Conference on Frontier of Computer Science and Technology, IEEE, pp.339-342, 2015.
6 H. Xiong, J. Wu, and J. Chen, "K-means Clustering versus Validation Measures: A Data Distribution Perspective," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, Issue 2, pp.779-784, 2009.
7 T. SHEIKH and S. AHRAWAL, "Enhanced K-means based Facial expressions recognition system," CSE Department CSIT Durg, Assistant Professor CSE Department CSIT Durg, pp. 39-41, May-2018.
8 T. Obichi and Y. Okaie, T. Nakano, T. Hara, S. Nishio, "Inbody Mobile Bionanosensor Networks Through Nondiffusion-based Molecular Communication," IEEE ICC (International Conference on Communications) 2015, pp. 1078-1084, 2015.
9 D.L. Davies and D.W. Bouldin, "A Cluster Separation Measure," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.1, pp.224-227, 1979.
10 J.C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters," J. Cybernetics, vol. 3, pp. 32- 57, 1973.   DOI
11 Size of Korea.[Internet]. Available: http://sizekorea.kats.go.kr.