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Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
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Data Mining and Knowledge Discovery
SPADE: An Efficient Algorithm for Mining Frequent Sequences
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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Pattern Recognition Letters
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ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
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Expert Systems with Applications: An International Journal
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CSSF-trie structure to mine constraint sequential patterns from progressive database
International Journal of Knowledge Engineering and Data Mining
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Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our missions well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.