Extracting Non-redundant Approximate Rules from Multi-level Datasets

Association rule mining plays an important job in knowledge and information discovery. Often the number of the discovered rules is huge and many of them are redundant, especially for multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work in focusing on single level datasets. Recent work by Shaw et. al. has extended the non-redundant approaches presented in to include the elimination of redundant exact basis rules from multi-level datasets. In this paper, we propose an extension to the work in to allow for the removal of hierarchically redundant approximate basis rules from multi-level datasets through the use of the datasetpsilas hierarchy or taxonomy. Experimentation shows our approach can effectively generate both multi-level and cross level non-redundant rule sets which are lossless.

[1]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[2]  Yuefeng Li,et al.  Mining Non-Redundant Association Rules Based on Concise Bases , 2007, Int. J. Pattern Recognit. Artif. Intell..

[3]  Ee-Peng Lim,et al.  Mining Multi-Level Rules with Recurrent Items using FP'-Tree , 2001 .

[4]  Gerd Stumme,et al.  Generating a Condensed Representation for Association Rules , 2005, Journal of Intelligent Information Systems.

[5]  M. Kaya,et al.  Mining multi-cross-level fuzzy weighted association rules , 2004, 2004 2nd International IEEE Conference on 'Intelligent Systems'. Proceedings (IEEE Cat. No.04EX791).

[6]  Raj P. Gopalan,et al.  CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth , 2003, ADC.

[7]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[8]  Yue Xu,et al.  Eliminating Redundant Association Rules in Multi-Level Datasets , 2008, DMIN.

[9]  Wee Keong Ng,et al.  Rapid association rule mining , 2001, CIKM '01.

[10]  Wynne Hsu,et al.  Multi-level organization and summarization of the discovered rules , 2000, KDD '00.

[11]  Yue Xu,et al.  Generating concise association rules , 2007, CIKM '07.

[12]  . R.C.Jain,et al.  Mining Level-Crossing Association Rules from Large Databases , 2006 .

[13]  Ferenc Bodon,et al.  A fast APRIORI implementation , 2003, FIMI.

[14]  Tzung-Pei Hong,et al.  Mining Fuzzy Multiple-Level Association Rules from Quantitative Data , 2004, Applied Intelligence.

[15]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[16]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[17]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

[18]  Mohammed J. Zaki.  Mining Non-Redundant Association Rules , 2004, Data Min. Knowl. Discov..

[19]  Jian Pei,et al.  Mining frequent patterns by pattern-growth: methodology and implications , 2000, SKDD.

[20]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[21]  Jiawei Han,et al.  Mining Multiple-Level Association Rules in Large Databases , 1999, IEEE Trans. Knowl. Data Eng..

[22]  Mohammed J. Zaki.  Generating non-redundant association rules , 2000, KDD '00.

[23]  Yue Xu,et al.  Concise representations for approximate association rules , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.