The week's pick
Random Articles
Reseach Article
Multilevel Relationship Algorithm for Association Rule Mining used for Cooperative Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 4 |
| Year of Publication: 2014 |
| Authors: Deepak A Vidhate, Parag Kulkarni |
10.5120/14973-3169
|
Deepak A Vidhate, Parag Kulkarni . Multilevel Relationship Algorithm for Association Rule Mining used for Cooperative Learning. International Journal of Computer Applications. 86, 4 ( January 2014), 18-27. DOI=10.5120/14973-3169
Abstract
Mining the Data is also known as Discovery of Knowledge in Databases. It is to get correlations, trends, patterns, anomalies from the databases which can help to build exact future decisions. However data mining is not the natural. No one can assure that the decision will lead to good quality results. It only helps experts to understand the data and show the way to good decisions. Association Mining is the discovery of relations or correlations among an item set. An objective is to make rules from given multiple sources of customer database transaction. It needs increasingly deepening knowledge mining process for finding refined knowledge from data. Earlier work is on mining association rules at one level. Though mining association rules at various levels is necessary. Finding of interesting association relationship among large amount of data will helpful to decision building, marketing, & business managing. For generating frequent item set we are using Apriori Algorithm in multiple levels so called Multilevel Relationship algorithm (MRA). MRA works in first two stages. In third stage of MRA uses Bayesian probability to find out the dependency & relationship among different shops, pattern of sales & generates the rule for learning. This paper gives detail idea about concepts of association mining, mathematical model development for Multilevel Relationship algorithm and Implementation & Result Analysis of MRA and performance comparison of MRA and Apriori algorithm.
References
- R. Agrawal, T. Imielinski, and A. Swami "Mining associations between sets of items in massive databases" In Proc. of the ACM SIGMOD Int'l Conference on Management of Data, 1993.
- R. Agrawal and R. Srikant "Fast algorithms for mining association rules in large databases" In Proceedings of the Twentieth International Conference on Very Large Databases, pages 487–499, Santiago, Chile, 1994.
- Mining Frequent Patterns without Candidate Generation - Jiawei Han, Jian Pei, Yiwen Yin
- Rakesh Agrawal, Christos Faloutsos, & Arun Swami "Efficient similarity search in sequence databases" In Proc. of the Fourth International Conference on Foundations of Data Organization and Algorithms, Chicago, October 1993. Also in Lecture Notes in Computer Science 730, Springer Verlag, 1993, 69-84.
- Rakesh Agrawal, SaktiGhosh, Tomasz Imielinski, BalaIyer, and Arun Swami "An interval classifer for database mining applications" Proc. of the VLDB Conference, pages 560-573, Vancouver, British Columbia, Canada, August 1992.
- Rakesh Agrawal, Tomasz Imielinski and Arun Swami "Database mining: A performance perspective" published in IEEE Transactions on Knowledge and Data Engineering, 5(6):914 925, December 1993. Special Issue on Learning and Discovery in Knowledge-Based Databases.
- Aaron Ceglar & John F. Roddick "Association Mining" in ACM Computing Surveys, Vol. 38, No. 2, Article 5, Publication date: July 2006.
- Baoqing Jiang,WeiWang and Yang Xu "The Math Background of Apriori Algorithm"
- Jiawei Han & Micheline Kamber "Data Mining: Concepts & Techniques" Second Edition, Elsevier publication.
- Pang-Ning Tan, Vipin Kumar & Michael Steinbach "Introduction to Data Mining" by Pearson Education Inc.
- Ethem Alpaydin "Introduction to Machine Learning" Second Edition, MIT Press by PHI.
- Tom Mitchell "Machine Learning" McGraw Hill International Edition.
- Kishor S. Trivedi "Probability & Statistics with Reliability, Queuing and Computer Science Applications" by PHI.
- Liviu Panait Sean Luke "Cooperative Multi-Agent Learning: The State of the Art", published in Journal of Autonomous Agents and Multi-Agent Systems Volume 11 Issue 3, pp. 387 – 434, 2005.
- Young-Cheol Choi, Student Member, Hyo-Sung Ahn "A Survey on Multi-Agent Reinforcement Learning: Coordination Problems", IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, pp. 81 – 86, 2010.
- Zahra Abbasi, Mohammad Ali Abbasi "Reinforcement Distribution in a Team of Cooperative Q-learning Agent", Proceedings of the 9th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, IEEE Computer Society, pp. 154-160, 2008.
- Babak Nadjar Araabi, Sahar Mastoureshgh, & Majid Nili Ahmadabadi "A Study on Expertise of Agents and Its Effects on Cooperative Q-Learning" ,IEEE Transactions on Evolutionary Computation, vol:14, pp:23-57, 2010.
- Jun-Yuan Tao, De-Sheng Li "Cooperative Strategy Learning In Multi-Agent Environment With Continuous State Space", IEEE International Conference on Machine Learning and Cybernetics, pp. 2107 – 2111, 2006.
- Dr. Hamid R. Berenji David Vengerov "Learning, Cooperation, and Coordination in Multi-Agent Systems", in Proceedings of 9th IEEE International Conference on Fuzzy Systems, 2000.
- M. V. Nagendra Prasad & Victor R. Lesser "Learning Situation-Specific Coordination in Cooperative Multi-agent Systems" in Journal of Autonomous Agents and Multi-Agent Systems, Volume 2 Issue 2, pp. 173 – 207, 1999.
- Edmund H Durfee, Victor R Lesser and Daniel D Corkill "Trends in Cooperative Distributed Problem Solving", IEEE Transactions on Knowledge and Data Engineering, 1995.
- Sandip Sen & Mahendra Sekaran "Individual learning of coordination knowledge", in Journal of Experimental & Theoretical Artificial Intelligence, vol. 10 issue3, pp. 333–356, 1998.
- Ronen Brafman & Moshe Tennenholtz "Learning to Coordinate Efficiently: A Model-based Approach", in Journal of Artificial Intelligence Research, Volume 19 Issue 1, pp. 11-23, 2003.
- Michael Kinney & Costas Tsatsoulis "Learning Communication Strategies in Multiagent Systems", in Journal of Applied Intelligence, Volume 9 Issue 1, pp 71-91, 1998.
- Georgios Chalkiadakis & Craig Boutilier "Coordination in Multiagent Reinforcement Learning: A Bayesian Approach" in AAMAS '03 Proceedings of the 2nd International Joint Conference on Autonomous agents and multiagent systems, pp. 709-716, 2003.
- Chern Han Yong & Risto Miikkulainen "Coevolution of Role-Based Cooperation in Multi-Agent Systems", in technical Report AI07-338, University of Texas at Austin, 2007.
- Hung H Bui Svetha Venkatesh and Dorota Kieronska "A Framework for Coordination and Learning among Team of Agents", in Agents and Multi-Agent Systems: Formalisms, Methodologies and Applications, Lecture Notes in Artificial Intelligence, Volume 1441, 1997.
- Jun Huang, N. R. Jennings & John Fox "An Agent Architecture for Distributed Medical Care" in Lecture Notes in Computer Science, Volume 890/1995, pp. 219-232, 1995.
- Thomas Haynes & Sandip Sen "Adaptation Using Cases in Cooperative Groups", in workshop proceedings of Association for the Advancement of Artificial Intelligence (AAAI), 1996.
- Richardson Ribeiro, André P. Borges and Fabrício Enembreck "Interaction Models for Multiagent Reinforcement Learning", in the CIMCA '08 Proceedings of IEEE International Conference on Computational Intelligence for Modeling Control & Automation, pp. 464-469, 2008.
- Herman Bruyninckx "Bayesian probability" ,Dept. of Mechanical Engineering, K. U. Leuven, Belgium, November 2002
- Chris Westbury "Bayes' For Beginners" Department of Psychology, P220 Biological Sciences Bldg. , University of Alberta, Edmonton, AB, T6G 2E9, Canada.
- Bruno A. Olshausen "Bayesian probability theory" March 1, 2004
- Toshiharu Sugawara & Victor Lesser "Learning to improve coordinated actions in cooperative distributed problem solving environments", in Journal of Machine Learning, Volume 33 Issue 2-3, pp. 129-153, 1998.
- Hamid Berenji & David Vengerov "Advantages of Cooperation between Reinforcement Learning Agents in difficult stochastic problems" in the 9th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 871 – 876, 2000.
Index Terms
Keywords