Bit Stream Mask-Search Algorithm in Frequent Itemset Mining

Association Rules in data mining are generated by identifying relationships among set of items in transaction database. Finding frequent itemsets is computationally the most expensive step in Association rule discovery and therefore it has attracted significant research attention. Although several techniques have emerged, they are all inherently dependent on the memory availability. This paper describes an efficient algorithmic approach called Bit Stream Mask Search which sorts the transaction database by transforming to numeric attributes. In the next step, frequent itemsets are found out, algorithms generated and the data hidden during the process time. During the search process, Masked Itemset Processing (MIP) searches the itemsets with a low execution time. Experimental evaluations show that this approach is faster and occupies less memory space during interaction compared to Apriori like and related algorithms.

[1]  Peng Yi-pu,et al.  Improvement of AprioriTid algorithm for mining association rules , 2005 .

[2]  Andrea Pietracaprina,et al.  Mining Frequent Itemsets using Patricia Tries , 2003, FIMI.

[3]  Hongjun Lu,et al.  H-mine: hyper-structure mining of frequent patterns in large databases , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[4]  Bart Goethals,et al.  Survey on Frequent Pattern Mining , 2003 .

[5]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[6]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[7]  Christian Borgelt,et al.  Induction of Association Rules: Apriori Implementation , 2002, COMPSTAT.

[8]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[9]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules , 1996, IEEE Trans. Knowl. Data Eng..

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

[11]  Gösta Grahne,et al.  Efficiently Using Prefix-trees in Mining Frequent Itemsets , 2003, FIMI.

[12]  Wen-Yang Lin,et al.  CBW: an efficient algorithm for frequent itemset mining , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[13]  Ming Lei,et al.  A high efficient AprioriTid algorithm for mining association rule , 2005, 2005 International Conference on Machine Learning and Cybernetics.