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A Survey of Clustering Algorithms in Association Rules Mining


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1 Applied Science Department, Ajloun University College, Balqa Applied University, Jordan
 

The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.
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  • Dhillon, I. S., Guan, Y. and Kulis, B. Kernel k-means: spectral clustering and normalized cuts. Proceeding of KDD '04 Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. Seattle, WA, USA — August 22 - 25, 2004.

  • Berkhin, P. A Survey of Clustering Data Mining Techniques. United States, North America: Springer, 2006. PP. 25 – 71.

  • AlZoubi, W. A. An Improved Clustered Based Technique for Frequent Items Generation from Transaction Datasets. CCIT 2018.

  • Moreira, A. Density-based clustering algorithms – DBSCAN and SNN. Version 1.0, 25.07.2005, University of Minho – Portugal.

  • Han, J., Cheng, H., Xin, D., & Yan, X. 2007. Frequent pattern mining: current status and future directions. Data Mining Knowledge Disc (2007), pp. 55–86.

  • Astashyn, A. 2004. Deterministic Data Reduction Methods for Transactional Datasets. Master Thesis. Polytechnic University. http://photon.poly.edu/~hbr/publi/alex_msthesis.pdf.

  • Pal N. R., Pal K., Keller J. M., and Bezdec J. C.2006. A possibilistic fuzzy c-means clustering algorithm. IEEE Transactions on Fuzzy Systems. Issue 4, Volume 13, August2005, pp. 517 – 530.

  • Alfred R. &Dimitar, K. 2007. A Clustering Approach to Generalized Pattern Identification Based on Multi-instanced Objects with DARA. In Local Proceedings of ADBIS. Varna. pp. 38 – 49.

  • Khan S. and Ahmad A. Cluster center initialization algorithm for K-means clustering. Pattern Recognition Letters. Volume 25, Issue 11, August 2004, pp. 1293 – 1302.

  • Fraley, C. Algorithms for Model-Based Gaussian Hierarchical Clustering. SIAM Journal on Scientific Computing, 1998, Vol. 20, No. 1. pp. 270-281.

  • Eyal Salman, H., Hammad, M., Seriai, A. and Al-Sbou, A. Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering. Information 2018, 9, 222; doi:10.3390/info9090222. www.mdpi.com/journal/information.

  • Heyer L, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9:1106–1115.

  • Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science 27 Jun 2014: Vol. 344, Issue 6191, pp. 1492-1496 DOI: 10.1126/science.1242072.

  • Song M, Christian W. Günther, Wil M. P. van der Aalst. Trace Clustering in Process Mining. International conference on Business Process Management (BPM 2008): Business Process Management Workshops pp 109-120.

  • T. Asano, B. Bhattacharya, M. Keil, and F. Yao. Clustering algorithms based on minimum and maximum spanning trees. In Proceedings of the 4th Annual Symposium on Computational Geometry, pages 252-257, 1988.

  • Tsay, Y.-J. & Chiang, J.-Y. 2005. CBAR: an efficient method for mining association rules. Knowledge-Based Systems 18 (2005), pp. 99–105.

  • Tsay, Y.-J. &Chien.-C, Y.-W. 2004. An efficient cluster and decomposition algorithm for mining association rules. Information Sciences 160 (2004) 161–171.

  • Hanirex, K &Rangaswamy, D. 2011. Efficient algorithm for miningfrequent itemsets using clustering techniques.International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 3 Mar 2011, pp. 1028 - 1032.


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  • A Survey of Clustering Algorithms in Association Rules Mining

Abstract Views: 236  |  PDF Views: 120

Authors

Wael Ahmad AlZoubi
Applied Science Department, Ajloun University College, Balqa Applied University, Jordan

Abstract


The main goal of cluster analysis is to classify elements into groupsbased on their similarity. Clustering has many applications such as astronomy, bioinformatics, bibliography, and pattern recognition. In this paper, a survey of clustering methods and techniques and identification of advantages and disadvantages of these methods are presented to give a solid background to choose the best method to extract strong association rules.

References