Mining Nonambiguous Temporal Patterns for Interval-Based Events

Previous research on mining sequential patterns mainly focused on discovering patterns from point-based event data. Little effort has been put toward mining patterns from interval-based event data, where a pair of time values is associated with each event. Kam and Fu's work in 2000 identified 13 temporal relationships between two intervals. According to these temporal relationships, a new variant of temporal patterns was defined for interval-based event data. Unfortunately, the patterns defined in this manner are ambiguous, which means that the temporal relationships among events cannot be correctly represented in temporal patterns. To resolve this problem, we first define a new kind of nonambiguous temporal pattern for interval-based event data. Then, the TPrefixSpan algorithm is developed to mine the new temporal patterns from interval-based events. The completeness and accuracy of the results are also proven. The experimental results show that the efficiency and scalability of the TPrefixSpan algorithm are satisfactory. Furthermore, to show the applicability and effectiveness of temporal pattern mining, we execute experiments to discover temporal patterns from historical Nasdaq data

[1]  Yi-Chung Hu,et al.  Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining , 2004, Inf. Sci..

[2]  Ping-Yu Hsu,et al.  Mining hybrid sequential patterns and sequential rules , 2002, Inf. Syst..

[3]  Donald E. Knuth,et al.  Fast Pattern Matching in Strings , 1977, SIAM J. Comput..

[4]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[5]  Jian Pei,et al.  Constrained frequent pattern mining: a pattern-growth view , 2002, SKDD.

[6]  Philip S. Yu.  IEEE Transactions on Knowledge and Data Engineering: EIC Editorial , 2001 .

[7]  David Wai-Lok Cheung,et al.  Efficient Algorithms for Incremental Update of Frequent Sequences , 2002, PAKDD.

[8]  Tao Luo,et al.  Using sequential and non-sequential patterns in predictive Web usage mining tasks , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[9]  Krithi Ramamritham,et al.  Discovering critical edge sequences in E-commerce catalogs , 2001, EC '01.

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

[11]  Yen-Liang Chen,et al.  Mining sequential patterns from multidimensional sequence data , 2005, IEEE Transactions on Knowledge and Data Engineering.

[12]  Philip S. Yu,et al.  HierarchyScan: a hierarchical similarity search algorithm for databases of long sequences , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[13]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[14]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

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

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

[17]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[18]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[19]  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).

[20]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

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

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

[23]  Jaideep Srivastava,et al.  Grouping Web page references into transactions for mining World Wide Web browsing patterns , 1997, Proceedings 1997 IEEE Knowledge and Data Engineering Exchange Workshop.

[24]  Valerie Guralnik,et al.  Parallel Tree Projection Algorithm for Sequence Mining , 2001, Euro-Par.

[25]  Ada Wai-Chee Fu,et al.  Discovering Temporal Patterns for Interval-Based Events , 2000, DaWaK.

[26]  Maguelonne Teisseire,et al.  Real time Web usage mining: a heuristic based distributed miner , 2001, Proceedings of the Second International Conference on Web Information Systems Engineering.

[27]  Yasufumi Takama,et al.  Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns , 2003, Inf. Process. Manag..

[28]  Jiawei Han,et al.  Mining Segment-Wise Periodic Patterns in Time-Related Databases , 1998, KDD.

[29]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

[30]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[31]  Marek Wojciechowski.  Interactive Constraint-Based Sequential Pattern Mining , 2001, ADBIS.

[32]  Joseph L. Hellerstein,et al.  Mining partially periodic event patterns with unknown periods , 2001, Proceedings 17th International Conference on Data Engineering.

[33]  Tadeusz Morzy,et al.  Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques , 2002, BalticDB&IS.

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

[35]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

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