MADPARM: Mobile Agent based Distributed and Parallel Association Rule Mining

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-49 Number-6
Year of Publication : 2017
Authors : A.Saleem Raja, E.George Dharma Prakash Raja
DOI :  10.14445/22315381/IJETT-V49P257


A.Saleem Raja, E.George Dharma Prakash Raja "MADPARM: Mobile Agent based Distributed and Parallel Association Rule Mining", International Journal of Engineering Trends and Technology (IJETT), V49(6),375-382 July 2017. ISSN:2231-5381. published by seventh sense research group

Distributed and Parallel data mining requires flexible and extensible framework to mining the useful knowledge from distributed database sites. Frequent Item-set mining is the primary step in association rule mining. Plenty of research work had been done in the distributed data mining especially association rule mining. Recently researcher deployed mobile agents for distributed association rule mining (IDMA, AEDM, AMAARM, EMADS, MADM , AeMSAR and MAD-ARM). Most of the approaches focused on reducing the communication cost by deploying multiple mobile agents and establish protocol for communication between them. This paper introduces the new novel framework which improves MADARM with parallel access of data from distributed site’s database using mobile agents (MADPARM). To improve the performance, we used compact bit table approach for mining frequent item-set (FI) from each site. Finally we present the result which shows the proposed framework gives better result than the MADARM in distributed environment.

[1]. L.Cao(ed.), Data Mining and Multi-agent Integration, DOI:10.1007/978-14419-0522-2_3, pp 47-58, Springer Science + Business Media, LLC 2009.
[2]. R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), Chile, 1994, pp. 487–499.
[3]. Benjamin, Rainer, Wolfgang, Memory-Efficient Frequent-Itemset Mining, EDBT 2011 ACM, Uppsala, Sweden.
[4]. Christian Borgelt. “An Implementation of the FP-growth Algorithm”.In International Workshop on Open Source Data Mining, 2005.
[5]. Christian Borgelt. “Efficient Implementations of Apriori and Eclat”.In Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.
[6]. Dong J, Han M., “BitTableFI: An efficient mining frequent itemsets algorithm,” Knowledge-Based Systems, vol.20 no.4, pp.329-335, 2007.
[7]. Song W, Yang B, Xu Z., “Index-BitTableFI: An improved algorithm for mining frequent itemsets,” Knowledge-Based Systems, vol.21, pp. 507–513. 2008.
[8]. Vo B, Hong T.P, Le B, “Dynamic Bit Vectors: An efficient approach for mining frequent itemsets,” Scientific Research and Essays, vol.6, pp.5358-5368, 2011.
[9]. Vo B, Hong T.P, Le B, “DBV-Miner: A Dynamic Bit Vector Approach for fast mining frequent closed itemsets,” Expert system with Applications, vol. 39, pp. 7196-7206, 2012.
[10]. Abonyi J., “A Novel Bitmap-Based Algorithm for Frequent Itemsets Mining,” Computational Intelligence in Engineering Studies in Computational Intelligence, vol. 313, pp. 171-180, 2010.
[11]. Saleem Raja.A ,George Dharma Prakash Raj, CBT-fi: Compact BitTable Approach for Mining Frequent Itemsets, ACSIJ Advances in Computer Science: an International Journal, Vol. 3, pp.72-76, Issue 5, No.11 , Sep 2014.
[12]. G.S.Bhamra, A.K.Verma and R.B.Patel,”Agent Enriched Distributed Association Rule Mining: A Review”. Springer Verlag Berlin Heidelberg, 2012.
[13]. Longbing Cao, Vladimir Gorodetsky, Pericles A. Mitkas, Agent Mining: The Synergy of Agents and Data Mining, IEEE Intelligent System,pp 64-72, 2009
[14]. Yun-Lan Wang, Zeng-Zhi Li and Hai-Ping Zhu, “Mobile Agent Based Distributed and Incremental Techniques for Association Rules”. In Proceeding of the Second International Conference on Machine Learning and Cybernetics”, 2003.
[15]. Aflori, C., Leon, F.: Efficient Distributed Data Mining using Intelligent Agents, Proceedings of the 8th International Symposium on Automatic Control and Computer Science (IASI), ISBN 973-621-086-3 (2004)
[16]. U.P.Kulkarni, P.D.Desai ,Tanveer Ahmed , J.V.Vadavi, A.R.Yardi, Mobile Agent Based Distributed Data Mining, International Conference on Computational Intelligence and Multimedia Applications , IEEE Computer Society 2007.
[17]. Gongzhu Hu and Shaozhen Ding, An Agent-Based Framework for Association Rules Mining of Distributed Data, Software Engineering Research, Management and Applications, SCI, vol- 253, pp 13-26, Springer-Verlag Heidelberg 2009.
[18]. Gongzhu Hu and Shaozhen Ding , Mining of Assocation Rules from Distributed Data using Mobile Agents, ICE-B 2009, PP 21-26 (2009).
[19]. A. O. Ogunde, Olusegun Folorunso, Adesina Simon Sodiya, On the Adaptivity of Distributed Association Rule Mining Agents, The Fourth International Conference on Adaptive and Self-Adaptive Systems and Applications, 2012.
[20]. A. O. Ogunde, O. Folorunso, A. S. Sodiya, and J. A. Oguntuase, “Towards an adaptive multi-agent architecture for association rule mining in distributed databases,” Adaptive Science and Technology (ICAST), 2011 3rd IEEE International Conference on 24-26 Nov. 2011, pp. 31 – 36 from IEEE Xplore.
[21]. Albashiri, K.A., Coenen, F.: Agent-Enriched Data Mining Using an Extendable Framework, ADMI 2009, LNCS 5680, PP 53-68 , Springer Verlag Berlin Heidelberg (2009).
[22]. Albashiri, K.A.: Agent Based Data Distribution for Parallel Association Rule Mining, International journal of computers, vol 8, 2014.
[23]. Walid Adly Atteya, Keshav Dahal and M.Alamgir Hossain, “Distributed BitTable multi-agent Association Rules Mining Algorithm” , Springer-Verlag, KES 2011, Part I, LNAI 6881.
[24]. Pham Nguyen Anh Huy, Ho Tu Bao, A distributed algorithm for mining association rules, Proceedings of The Third International Conference on Parallel and Distributed Computing, Applications and Technologies 2002.
[25]. Saleem, George, “MAD-ARM: Mobile Agent based Distributed Association Rule Mining”, ICCCI’13, IEEE Conference 2013.
[26]. Saleem, George, Mobile Agent based Distributed Association Rule Mining: A Survey, 2nd International Conference on Applied Information and Communications Technology(ICAICT) –Elsevier – 2014.
[27]. Saleem, George, Compact BitTable based Distributed Association Rule Mining using Mobile Agent Framework, European Journal of Scientific Research, Volume 128 No 4, January, 2015.

Distributed Association Rule Mining, Frequent Item Set Mining, Parallel Mining, Mobile agent based distributed FI mining. CBT-FI based distributed association rule mining, Compact BitTable based FI.