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Closed inter-sequence pattern mining
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AI
DOI：10.1016/j.jss.2013.02.010.png)
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Abstract
En 中文
Inter-sequence pattern mining can find associations across several sequences in a sequence database, which can discover both a sequential pattern within a transaction and sequential patterns across several different transactions. However, inter-sequence pattern mining algorithms usually generate a large number of recurrent frequent patterns. We have observed mining closed inter-sequence patterns instead of frequent ones can lead to a more compact yet complete result set. Therefore, in this paper, we propose a model of closed inter-sequence pattern mining and an efficient algorithm called CISP-Miner for mining such patterns, which enumerates closed inter-sequence patterns recursively along a search tree in a depth-first search manner. In addition, several effective pruning strategies and closure checking schemes are designed to reduce the search space and thus accelerate the algorithm. Our experiment results demonstrate that the proposed CISP-Miner algorithm is very efficient and outperforms a compared EISP-Miner algorithm in most cases. (C) 2013 Elsevier Inc. All rights reserved.
Keywords:
Closed patterns
Data mining
Inter-sequence pattern
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