Meta itemset: a new concise representation of frequent itemset

The sheer size of all frequent itemsets is one challenging problem in data mining research. Based on both closed itemset and maximal itemset, meta itemset which is a new concise representation of frequent itemset is proposed. It is proved that both closed itemset and maximal itemset are special cases of meta itemset. The set of all closed itemsets and the set of all maximal itemsets form the upper bound and the lower bound of the set of all meta itemsets. Then, property and pruning strategies of meta itemset are discussed. Finally, an efficient algorithm for mining meta itemset is proposed. Experimental results show that the proposed algorithm is effective and efficient.

[1]  Salvatore Orlando,et al.  Fast and memory efficient mining of frequent closed itemsets , 2006, IEEE Transactions on Knowledge and Data Engineering.

[2]  Laks V. S. Lakshmanan,et al.  Pushing Convertible Constraints in Frequent Itemset Mining , 2004, Data Mining and Knowledge Discovery.

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

[4]  E. Meijering,et al.  A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[5]  P. S. Sastry,et al.  Discovering Frequent Generalized Episodes When Events Persist for Different Durations , 2007, IEEE Transactions on Knowledge and Data Engineering.

[6]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[7]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[8]  Sanasam Ranbir Singh,et al.  An algorithm for discovering the frequent closed itemsets in a large database , 2006, J. Exp. Theor. Artif. Intell..

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

[10]  Salvatore Orlando,et al.  Enhancing the Apriori Algorithm for Frequent Set Counting , 2001, DaWaK.

[11]  Hongjun Lu,et al.  Ascending frequency ordered prefix-tree: efficient mining of frequent patterns , 2003, Eighth International Conference on Database Systems for Advanced Applications, 2003. (DASFAA 2003). Proceedings..

[12]  Aristides Gionis,et al.  Approximating a collection of frequent sets , 2004, KDD.

[13]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[14]  Ping Guo,et al.  Frequent mining of subgraph structures , 2006, J. Exp. Theor. Artif. Intell..

[15]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[16]  Gösta Grahne,et al.  Fast algorithms for frequent itemset mining using FP-trees , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[18]  Hongjun Lu,et al.  Efficient Mining of Frequent Patterns Using Ascending Frequency Ordered Prefix-Tree , 2004, Data Mining and Knowledge Discovery.

[19]  Toon Calders,et al.  Non-derivable itemset mining , 2007, Data Mining and Knowledge Discovery.

[20]  Bingru Yang,et al.  Index-Maxminer: a New Maximal Frequent Itemset Mining Algorithm , 2008, Int. J. Artif. Intell. Tools.

[21]  E. Meijering.  A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[22]  Marzena Kryszkiewicz.  Generalized disjunction-free representation of frequent patterns with negation , 2005, J. Exp. Theor. Artif. Intell..

[23]  Rokia Missaoui,et al.  Generating frequent itemsets incrementally: two novel approaches based on Galois lattice theory , 2002, J. Exp. Theor. Artif. Intell..

[24]  Jian Pei,et al.  Sequence Data Mining , 2007, Advances in Database Systems.

[25]  Gregory Piatetsky-Shapiro,et al.  Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from “university” to “business” and “analytics” , 2007, Data Mining and Knowledge Discovery.