(TD)2PaM: A Constraint-Based Algorithm for Mining Temporal Patterns in Transactional Databases

The analysis of frequent behaviors regarding temporal issues begins to achieve some interest, in particular in the area of health care. However, existing approaches tend to ignore the temporal information and only make use of the order among events occurrence. In this paper, we introduce the notion of temporal constraint, and propose three instantiations of it: complete cyclic temporal constraints, partial cyclic temporal constraints and timespan constraints. Additionally, we propose a new algorithm – (TD)2PaM, that together with these constraints, makes possible to focus the pattern mining process on looking for cyclic and timespan patterns. Experimental results reveal the algorithm to be as efficient as its predecessors, and able to discover more informed patterns.

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

[2]  Georges Gardarin,et al.  Advances in Database Technology — EDBT '96 , 1996, Lecture Notes in Computer Science.

[3]  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).

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

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

[6]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[7]  Ming-Tat Ko,et al.  Discovering time-interval sequential patterns in sequence databases , 2003, Expert Syst. Appl..

[8]  Richard Fikes,et al.  A Reusable Time Ontology , 2002 .

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

[10]  Theophano Mitsa,et al.  Temporal Data Mining , 2010 .

[11]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[12]  Ming-Syan Chen,et al.  Twain: Two-end association miner with precise frequent exhibition periods , 2007, TKDD.

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

[14]  Cláudia Antunes,et al.  Mining Patterns with Domain Knowledge: A Case Study on Multi-language Data , 2012, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science.

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

[16]  Ming-Syan Chen,et al.  Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules , 2003, IEEE Trans. Knowl. Data Eng..