High-utility pattern mining: A method for discovery of high-utility item sets |
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Authors: | Jianying Hu [Author Vitae] Aleksandra Mojsilovic [Author Vitae] |
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Affiliation: | IBM T.J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, NY 10598, USA |
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Abstract: | We present an algorithm for frequent item set mining that identifies high-utility item combinations. In contrast to the traditional association rule and frequent item mining techniques, the goal of the algorithm is to find segments of data, defined through combinations of few items (rules), which satisfy certain conditions as a group and maximize a predefined objective function. We formulate the task as an optimization problem, present an efficient approximation to solve it through specialized partition trees, called High-Yield Partition Trees, and investigate the performance of different splitting strategies. The algorithm has been tested on “real-world” data sets, and achieved very good results. |
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Keywords: | High-utility item sets Pattern mining Partition tree |
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