An Efficient Market Basket Analysis based on Adaptive Association Rule Mining with Faster Rule Generation Algorithm

—Data mining is the process of extracting relatively useful information from a large data base. Majority of the recognized business organizations, super markets, etc., have accumulated huge amount of information from their customers. A vital sub problem of data mining is to identify frequent sets to assist mine association rules for Market Basket Analysis (MBA). Market Basket Analysis is an effective data mining tool utilized to discover the co-occurrence or coexistence of nominal or categorical observations. MBA is extensively used to identify purchasing pattern of customers in a supermarkets using transaction level data. However, it is very tough to find the valuable information hidden in large databases. Many researches were done by the database community based on association rule mining and classification technique to find the related information in large databases. The most widely used technique to conduct Market Basket Analysis is association rules technique. In this paper, an effective MBA based on Adaptive Association Rule Mining with Faster Rule Generation Algorithm (FRG-AARM) is proposed based on Adaptive Association Rule Mining. This algorithm speeds up the rule mining process with better accuracy and effectiveness.

[1]  Priscilla S. Markwood,et al.  The Long Tail: Why the Future of Business is Selling Less of More , 2006 .

[2]  Lin Shi,et al.  Mining Association Rules Based on Apriori Algorithm and Application , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[3]  Keith C. C. Chan,et al.  Market-basket analysis with principal component analysis: an exploration , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[4]  Przemyslaw Kazienko,et al.  The influence of indirect association rules on recommendation ranking lists , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[5]  LinWeiyang,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2002 .

[6]  Deren Li,et al.  Association rule mining based on concept lattice , 2005 .

[7]  Xie Wen-xiu,et al.  Market Basket Analysis Based on Text Segmentation and Association Rule Mining , 2010, 2010 First International Conference on Networking and Distributed Computing.

[8]  Sergio A. Alvarez,et al.  Collaborative Recommendation via Adaptive Association Rule Mining , 2000 .

[9]  C. S. Kanimozhi Selvi,et al.  Association Rule Mining with Dynamic Adaptive Support Thresholds for Associative Classification , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[10]  Caihui Liu,et al.  Multi-dimension association rule mining based on Adaptive Genetic Algorithm , 2011, 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering.

[11]  Sergio A. Alvarez,et al.  Efficient Adaptive-Support Association Rule Mining for Recommender Systems , 2004, Data Mining and Knowledge Discovery.

[12]  Nandlal L. Sarda,et al.  An adaptive algorithm for incremental mining of association rules , 1998, Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130).

[13]  Sally Jo Cunningham,et al.  Market basket analysis of library circulation data , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

[14]  Yasufumi Takama,et al.  Mining Association Rules for Adaptive Search Engine Based on RDF Technology , 2007, IEEE Transactions on Industrial Electronics.

[15]  Tzung-Pei Hong,et al.  Efficient generation of Adaptive-Support Association Rules , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[16]  Andrej Trnka,et al.  Market Basket Analysis with Data Mining methods , 2010, 2010 International Conference on Networking and Information Technology.

[17]  K. Shimada,et al.  Self-Adaptive Mechanism in Genetic Network Programming for Mining Association Rules , 2006, 2006 SICE-ICASE International Joint Conference.