Mining Maximum Length Frequent Itemsets: A Summary of Results

The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present a novel algorithm to solve this problem. Indeed, maximum length frequent itemsets can be efficiently identified in very large data sets and are useful in many application domains. Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. Also, a number of optimization techniques have been exploited to prune the search space. Our experimental results show that our algorithm is very efficient and scalable

[1]  Ulrich Güntzer,et al.  Algorithms for association rule mining — a general survey and comparison , 2000, SKDD.

[2]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[3]  Jitender S. Deogun,et al.  Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[4]  Hui Xiong,et al.  Hyperclique pattern discovery , 2006, Data Mining and Knowledge Discovery.

[5]  Devavrat Shah,et al.  Turbo-charging vertical mining of large databases , 2000, SIGMOD '00.

[6]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[7]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

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

[9]  Mohammed J. Zaki,et al.  CHARM: An Efficient Algorithm for Closed Itemset Mining , 2002, SDM.

[10]  Mohammed J. Zaki,et al.  Efficiently mining maximal frequent itemsets , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[11]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[12]  Mohammed J. Zaki,et al.  Efficient algorithms for mining closed itemsets and their lattice structure , 2005, IEEE Transactions on Knowledge and Data Engineering.

[13]  Ron Rymon,et al.  Search through Systematic Set Enumeration , 1992, KR.

[14]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[15]  Osmar R. Zaïane,et al.  Incremental mining of frequent patterns without candidate generation or support constraint , 2003, Seventh International Database Engineering and Applications Symposium, 2003. Proceedings..

[16]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.

[17]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

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

[19]  Zvi M. Kedem,et al.  Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set , 1998, EDBT.

[20]  Simon Fraser.  MULTI-DIMENSIONAL SEQUENTIAL PATTERN MINING , 2001 .

[21]  Hui Xiong,et al.  Mining strong affinity association patterns in data sets with skewed support distribution , 2003, Third IEEE International Conference on Data Mining.

[22]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[23]  Marek Wojciechowski,et al.  Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining , 2002, Pattern Detection and Discovery.

[24]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[25]  Ramesh C Agarwal,et al.  Depth first generation of long patterns , 2000, KDD '00.

[26]  G. Grahne,et al.  High Performance Mining of Maximal Frequent Itemsets Gösta , 2003 .

[27]  Johannes Gehrke,et al.  MAFIA: a maximal frequent itemset algorithm for transactional databases , 2001, Proceedings 17th International Conference on Data Engineering.

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