Data Mining and Knowledge Engineering

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Survey on Algorithms Applied in Pattern Mining


Affiliations
1 Nehru Arts and Science College, T.M.Palayam, Coimbatore, India
2 Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, India
     

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Data mining is a collection of techniques to extract hidden and potentially useful information from large databases of various business domains. For identifying the interesting patterns and co-relation and to get benefits from the repository data, Association Rule Mining (ARM) methods are used. Pattern recognition is a major challenge within the field of data mining and knowledge discovery. In this paper, a range of widely used algorithms are analyzed for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. The paper also focuses on each of the algorithm’s strengths and weaknesses for finding patterns in different transactional dataset.
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  • Sourav S. Bhowmick Qiankun Zhao, "Association Rule Mining: A Survey," Nanyang Technological University, Singapore.

  • Jiawei Han • Hong Cheng • Dong Xin • Xifeng Yan, "Frequent pattern mining: current status and future Directions," Data Mining Knowl Discov, vol. 15, no. I, p. 32, 2007.

  • Iqbal Gondal and Joarder Kamruzzaman Md. Mamunur Rashid, "Mining Associated Sensor Pattern for data stream of wireless networks," in PM2HW2N '13, Spain, 2013.

  • Chistopher.T, PhD Saravanan Suba, "A Study on Milestones of Association Rule Mining," International Journal of Computer Applications, p. 7, June 2012.

  • WeeKeong, YewKwong Amitabha Das, "Rapid Association Rule Mining," in Information and Knowledge Management, Atlanta, Georgia, 2001, pp. 474-481.


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  • Survey on Algorithms Applied in Pattern Mining

Abstract Views: 313  |  PDF Views: 2

Authors

M. V. Jisha
Nehru Arts and Science College, T.M.Palayam, Coimbatore, India
D. Vimal Kumar
Department of Computer Science, Nehru Arts and Science College, T.M.Palayam, Coimbatore, India

Abstract


Data mining is a collection of techniques to extract hidden and potentially useful information from large databases of various business domains. For identifying the interesting patterns and co-relation and to get benefits from the repository data, Association Rule Mining (ARM) methods are used. Pattern recognition is a major challenge within the field of data mining and knowledge discovery. In this paper, a range of widely used algorithms are analyzed for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. The paper also focuses on each of the algorithm’s strengths and weaknesses for finding patterns in different transactional dataset.

References