Discovering Frequent Generalized Episodes When Events Persist for Different Durations

This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined, which extends this framework by incorporating event duration constraints explicitly into the pattern's definition. This new formalism facilitates extension of the technique of episodes discovery to applications where data appears as a sequence of events that persist for different durations (rather than being instantaneous). We present efficient algorithms for episode discovery in this new framework. Through extensive simulations, we show the expressive power of the new formalism. We also show how the duration constraint possibilities can be used as a design choice to properly focus the episode discovery process. Finally, we briefly discuss some interesting results obtained on data from manufacturing plants of General Motors.

[1]  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).

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

[3]  Mikhail J. Atallah,et al.  Detection of significant sets of episodes in event sequences , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[4]  K. P. Unnikrishnan,et al.  Fast algorithms for frequent episode discovery in event sequences , 2004 .

[5]  Srivatsan Laxman.  Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations , 2006 .

[6]  Ayumi Shinohara,et al.  A Practical Algorithm to Find the Best Episode Patterns , 2001, Discovery Science.

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

[8]  Heikki Mannila,et al.  Levelwise Search and Borders of Theories in Knowledge Discovery , 1997, Data Mining and Knowledge Discovery.

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

[10]  P. S. Sastry,et al.  Discovering frequent episodes and learning hidden Markov models: a formal connection , 2005, IEEE Transactions on Knowledge and Data Engineering.

[11]  Dimitrios Gunopulos,et al.  Episode Matching , 1997, CPM.

[12]  Gemma C. Garriga.  Discovering Unbounded Episodes in Sequential Data , 2003, PKDD.

[13]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.