Maintenance of Frequent Patterns: A Survey

AbstrAct This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed

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

[2]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[3]  David Wai-Lok Cheung,et al.  A General Incremental Technique for Maintaining Discovered Association Rules , 1997, DASFAA.

[4]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

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

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

[7]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[8]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

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

[10]  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.

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

[12]  Mohammed J. Zaki,et al.  Mining Frequent Itemsets in Evolving Databases , 2002, SDM.

[13]  D. Cheung,et al.  Maintenance of Discovered Association Rules , 2002 .

[14]  Erik D. Demaine,et al.  Frequency Estimation of Internet Packet Streams with Limited Space , 2002, ESA.

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

[17]  Jian Pei,et al.  CLOSET+: searching for the best strategies for mining frequent closed itemsets , 2003, KDD '03.

[18]  Jiawei Han,et al.  MAIDS: mining alarming incidents from data streams , 2004, SIGMOD '04.

[19]  Rajeev Motwani,et al.  Scalable Techniques for Mining Causal Structures , 1998, Data Mining and Knowledge Discovery.

[20]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

[21]  Yonatan Aumann,et al.  Borders: An Efficient Algorithm for Association Generation in Dynamic Databases , 1999, Journal of Intelligent Information Systems.

[22]  Jia-Ling Koh,et al.  An Efficient Approach for Maintaining Association Rules Based on Adjusting FP-Tree Structures1 , 2004, DASFAA.

[23]  Ming-Syan Chen,et al.  Sliding window filtering: an efficient method for incremental mining on a time-variant database , 2005, Inf. Syst..

[24]  Le Gruenwald,et al.  Estimating Missing Values in Related Sensor Data Streams , 2005, COMAD.

[25]  Divyakant Agrawal,et al.  Efficient Computation of Frequent and Top-k Elements in Data Streams , 2005, ICDT.

[26]  Jinyan Li,et al.  Relative risk and odds ratio: a data mining perspective , 2005, PODS '05.

[27]  Carson Kai-Sang Leung,et al.  DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams , 2006, Sixth International Conference on Data Mining (ICDM'06).

[28]  Nan Jiang,et al.  Research issues in data stream association rule mining , 2006, SGMD.

[29]  Zhan Li,et al.  Knowledge and Information Systems , 2007 .

[30]  Jinyan Li,et al.  Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed , 2007, PAKDD.