TSET MAX : An Algorithm for Mining Frequent Maximal Temporal Patterns

The incorporation of temporal semantics into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, in a lot of cases is practically a computationally intractable problem and therefore it poses more challenges on efficient processing than non-temporal techniques. Based in the inter-transactional framework, in [13] we proposed an algorithm named TSET for mining temporal patterns (sequences) from datasets. One of the main drawbacks of this algorithm is the fact that it is an Aprioristyle and therefore, for each frequent pattern, all subsets of it need to be generated. In datasets with long patterns, this could degenerate into a computationally unfeasible problem. To address this problem, in this paper we present a new algorithm named TSET for mining frequent maximal temporal patterns. This algorithm extract the patterns using a lookahead technique and a depth first search on a temporal set-enumeration tree.

[1]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[2]  John F. Roddick,et al.  A Survey of Temporal Knowledge Discovery Paradigms and Methods , 2002, IEEE Trans. Knowl. Data Eng..

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

[4]  Hongjun Lu,et al.  Stock movement prediction and N-dimensional inter-transaction association rules , 1998, SIGMOD 1998.

[5]  Anthony K. H. Tung,et al.  Efficient Mining of Intertransaction Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[6]  Hongjun Lu,et al.  A template model for multidimensional inter-transactional association rules , 2002, The VLDB Journal.

[7]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2003 .

[8]  Nicolás Marín,et al.  TBAR: An efficient method for association rule mining in relational databases , 2001, Data Knowl. Eng..

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

[10]  Sushil Jajodia,et al.  Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract) , 1996, PODS.

[11]  Gustavo Rossi,et al.  An approach to discovering temporal association rules , 2000, SAC '00.

[12]  Heikki Mannila,et al.  Local and Global Methods in Data Mining: Basic Techniques and Open Problems , 2002, ICALP.

[13]  Hongjun Lu,et al.  Beyond intratransaction association analysis: mining multidimensional intertransaction association rules , 2000, TOIS.

[14]  Charu C. Aggarwal,et al.  A Tree Projection Algorithm for Generation of Frequent Item Sets , 2001, J. Parallel Distributed Comput..

[15]  Carlos Ordonez,et al.  Discovering Interesting Association Rules in Medical Data , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[16]  Anthony K. H. Tung,et al.  Breaking the barrier of transactions: mining inter-transaction association rules , 1999, KDD '99.

[17]  Charu C. Aggarwal,et al.  Towards long pattern generation in dense databases , 2001, SKDD.

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

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

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

[21]  Zvi M. Kedem,et al.  Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set , 2002, IEEE Trans. Knowl. Data Eng..

[22]  Keun Ho Ryu,et al.  Discovering Temporal Relation Rules Mining from Interval Data , 2002, EurAsia-ICT.

[23]  G. P. Bhattacharjee,et al.  Temporal representation and reasoning in artificial intelligence: A review , 2001 .

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

[25]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[26]  Frans Coenen,et al.  Tree Structures for Mining Association Rules , 2004, Data Mining and Knowledge Discovery.

[27]  Zhi-Hua Zhou,et al.  Three perspectives of data mining , 2003, Artif. Intell..

[28]  Ming-Syan Chen,et al.  On mining general temporal association rules in a publication database , 2001, Proceedings 2001 IEEE International Conference on Data Mining.