Abstract
Frequent Itemset Mining, or just pattern mining, plays an important role in data mining, aiming for the discovery of frequent co-occurrences in data. However, existing techniques still suffer from two bottlenecks that difficult the analysis and actual application of their results: they usually return a large number of patterns, and these patterns usually do not reflect user expectations. The most accepted and common approach to minimize these drawbacks is to define the user needs through constraints, and use them to filter and return less but more interesting patterns. Several types of constraints have been proposed in the literature, along with some algorithms that are able to incorporate them. However, there is no unified algorithm able to push any type of constraint. In this work we propose to push constraints into pattern mining through the use of a pattern-tree structure to efficiently store, check and prune the patterns. We define in detail a set of strategies to push each type of constraint, and a generic algorithm that is able to combine these strategies and incorporate any constraint into a pattern-tree.
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Silva, A., Antunes, C. (2013). Pushing Constraints into a Pattern-Tree. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_13
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DOI: https://doi.org/10.1007/978-3-642-41550-0_13
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