Pattern-Growth Methods

Mining frequent patterns has been a focused topic in data mining research in recent years, with the development of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Many studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns.

[1]  Heikki Mannila,et al.  Efficient Algorithms for Discovering Association Rules , 1994, KDD Workshop.

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

[3]  Jian Pei,et al.  Can we push more constraints into frequent pattern mining? , 2000, KDD '00.

[4]  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).

[5]  Umeshwar Dayal,et al.  FreeSpan: frequent pattern-projected sequential pattern mining , 2000, KDD '00.

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

[7]  Laks V. S. Lakshmanan,et al.  Mining frequent itemsets with convertible constraints , 2001, Proceedings 17th International Conference on Data Engineering.

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

[9]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

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

[11]  Jiawei Han,et al.  gApprox: Mining Frequent Approximate Patterns from a Massive Network , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[12]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

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

[14]  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.

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

[16]  Jiawei Han,et al.  Discriminative Frequent Pattern Analysis for Effective Classification , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

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

[18]  Jian Pei,et al.  CMAR: accurate and efficient classification based on multiple class-association rules , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[19]  Christos Faloutsos,et al.  Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining , 1998, VLDB.

[20]  Philip S. Yu,et al.  MaPle: a fast algorithm for maximal pattern-based clustering , 2003, Third IEEE International Conference on Data Mining.

[21]  Umeshwar Dayal,et al.  Multi-dimensional sequential pattern mining , 2001, CIKM '01.

[22]  Laks V. S. Lakshmanan,et al.  Optimization of constrained frequent set queries with 2-variable constraints , 1999, SIGMOD '99.

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

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

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

[26]  Raghu Ramakrishnan,et al.  Bottom-up computation of sparse and Iceberg CUBE , 1999, SIGMOD '99.

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

[28]  Sridhar Ramaswamy,et al.  Cyclic association rules , 1998, Proceedings 14th International Conference on Data Engineering.

[29]  Jiawei Han,et al.  CloseGraph: mining closed frequent graph patterns , 2003, KDD '03.

[30]  Rajeev Motwani,et al.  Computing Iceberg Queries Efficiently , 1998, VLDB.

[31]  Philip S. Yu,et al.  Direct Discriminative Pattern Mining for Effective Classification , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[32]  Laks V. S. Lakshmanan,et al.  Efficient mining of constrained correlated sets , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[33]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[34]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[35]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[36]  Philip S. Yu,et al.  Mining Colossal Frequent Patterns by Core Pattern Fusion , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

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

[38]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.