From sequential pattern mining to structured pattern mining: A pattern-growth approach

Sequential pattern mining is an important data mining problem with broad applications. However, it is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Recent studies have developed two major classes of sequential pattern mining methods: (1) acandidate generation-and-test approach, represented by (i) GSP, a horizontal format-based sequential pattern mining method, and (ii) SPADE, a vertical format-based method; and (2) apattern-growth method, represented by PrefixSpan and its further extensions, such as gSpan for mining structured patterns.In this study, we perform a systematic introduction and presentation of the pattern-growth methodology and study its principles and extensions. We first introduce two interesting pattern-growth algorithms, FreeSpan and PrefixSpan, for efficient sequential pattern mining. Then we introduce gSpan for mining structured patterns using the same methodology. Their relative performance in large databases is presented and analyzed. Several extensions of these methods are also discussed in the paper, including mining multi-level, multi-dimensional patterns and mining constraint-based patterns.

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

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

[3]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

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

[6]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[7]  Jian Pei,et al.  Efficient computation of Iceberg cubes with complex measures , 2001, SIGMOD '01.

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

[9]  ZhengZijian,et al.  KDD-Cup 2000 organizers' report , 2000 .

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

[11]  A. Akhmetova.  Discovery of Frequent Episodes in Event Sequences , 2006 .

[12]  George Karypis,et al.  Frequent subgraph discovery , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

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

[14]  Mohammed J. Zaki.  Efficient enumeration of frequent sequences , 1998, CIKM '98.

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

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

[17]  Florent Masseglia,et al.  The PSP Approach for Mining Sequential Patterns , 1998, PKDD.

[18]  Hiroki Arimura,et al.  Efficient Substructure Discovery from Large Semi-Structured Data , 2001, IEICE Trans. Inf. Syst..

[19]  Dimitrios Gunopulos,et al.  Constraint-Based Rule Mining in Large, Dense Databases , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

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

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

[22]  Laks V. S. Lakshmanan,et al.  On dual mining: from patterns to circumstances, and back , 2001, Proceedings 17th International Conference on Data Engineering.

[23]  Kaizhong Zhang,et al.  Combinatorial pattern discovery for scientific data: some preliminary results , 1994, SIGMOD '94.

[24]  Carla E. Brodley,et al.  KDD-Cup 2000 organizers' report: peeling the onion , 2000, SKDD.

[25]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[26]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[27]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

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

[29]  Jian Pei,et al.  Mining sequential patterns with constraints in large databases , 2002, CIKM '02.

[30]  Sushil Jajodia,et al.  Mining Temporal Relationships with Multiple Granularities in Time Sequences , 1998, IEEE Data Eng. Bull..

[31]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[32]  Jiawei Han,et al.  Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes , 1997, KDD.

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

[34]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

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

[36]  Ehud Gudes,et al.  Computing frequent graph patterns from semistructured data , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[37]  Sridhar Ramaswamy,et al.  On the Discovery of Interesting Patterns in Association Rules , 1998, VLDB.

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

[39]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

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