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• W2100139528 abstract "In this paper, we describe a set-based approach for mining association rules and finding frequent sequential patterns in customer transactional databases. The set-based approach is a direct improvement of the original association rule mining algorithms proposed by R. Agrawal and R. Skrikant. Our approach relaxes the constraints described in Apriori (All/Some), and improves the performance while being more user-oriented and self-adaptive than the probabilistic knowledge representation. We compare the performance of the improved algorithms with results from an experimental study. The approach can be extended to more set-based mathematical models for further data analysis in order to discover hidden knowledge and patterns with the improved workflow and set-based representation." @default.
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• W2100139528 date "2009-09-01" @default.
• W2100139528 modified "2023-09-24" @default.
• W2100139528 title "Set-based approach in mining sequential patterns" @default.
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• W2100139528 doi "https://doi.org/10.1109/iscis.2009.5291851" @default.
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