Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40932
Title: Mining and ranking generators of sequential patterns
Authors: Lo, D.
Khoo, S.-C. 
Li, J.
Issue Date: 2008
Source: Lo, D.,Khoo, S.-C.,Li, J. (2008). Mining and ranking generators of sequential patterns. Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130 2 : 553-564. ScholarBank@NUS Repository.
Abstract: Sequential pattern mining first proposed by Agrawal and Srikant has received intensive research due to its wide range applicability in many real-life domains. Various improvements have been proposed which include mining a closed set of sequential patterns. Sequential patterns supported by the same sequences in the database can be considered as belonging to an equivalence class. Each equivalence class contains patterns partially-ordered by sub-sequence relationship and having the same support. Within an equivalence class, the set of maximal and minimal patterns are referred to as closed patterns and generators respectively. Generators used together with closed patterns can provide additional Information which closed patterns alone are not able to provide. Also, as generators are the minimal members, they are preferable over closed patterns for model selection and classification based on the Minimum Description Length (MDL) principle. Several algorithms have been proposed for mining closed sequential patterns, but none so far for mining sequential generators. This paper fills this research gap by investigating properties of sequential generators and proposing an algorithm to efficiently mine sequential generators. The algorithm works on a three-step process of search space compaction, non-generator pruning and a final filtering step. We also introduce ranking of mined generators and propose mining of a unique generator per equivalence class. Performance study has been conducted on various synthetic and real benchmark datasets. They show that mining generators can be as fast as mining closed patterns even at low support thresholds. Copyright © by SIAM.
Source Title: Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130
URI: http://scholarbank.nus.edu.sg/handle/10635/40932
ISBN: 9781605603179
Appears in Collections:Staff Publications

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