Discovering Richer Temporal Association Rules from Interval-Based Data

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm to discover frequent temporal patterns and to generate richer interval-based temporal association rules.

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

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

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

[4]  George Koundourakis,et al.  Association Rules & Evolution in Time , 2002, SETN.

[5]  Suh-Yin Lee,et al.  Fast Discovery of Sequential Patterns by Memory Indexing , 2002, DaWaK.

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

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

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

[9]  F. Höppner.  Learning Temporal Rules from State Sequences , 2001 .

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

[11]  John F. Roddick,et al.  Linear temporal sequences and their interpretation using midpoint relationships , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

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

[14]  Kien A. Hua,et al.  Mining Interval Time Series , 1999, DaWaK.

[15]  Christian Freksa,et al.  Temporal Reasoning Based on Semi-Intervals , 1992, Artif. Intell..

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

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

[18]  Heikki Mannila,et al.  Discovering Generalized Episodes Using Minimal Occurrences , 1996, KDD.

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

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

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

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