篇名 |
IDHUP: Incremental Discovery of High Utility Pattern
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並列篇名 | IDHUP: Incremental Discovery of High Utility Pattern |
作者 | Lele Yu、Wensheng Gan、Zhixiong Chen、Yining Liu |
英文摘要 | As a sub-problem of pattern discovery, utility-oriented pattern mining has recently emerged as a focus of researchers’ attention and offers broad application prospects. Considering the dynamic characteristics of the input databases, incremental utility mining methods have been proposed, aiming to discover implicit information/ patterns whose importance/utility is not less than a user-specified threshold from incremental databases. However, due to the explosive growth of the search space, most existing methods perform unsatisfactorily under the low utility threshold, so there is still room for improvement in terms of running efficiency and pruning capacity. Motivated by this, we provide an effective and efficient method called IDHUP by designing an indexed partitioned utility list structure and employing four pruning strategies. With the proposed data structure, IDHUP can not only dynamically update the utility values of patterns but also avoid visiting non-occurred patterns. Moreover, to further exclude ineligible patterns and avoid unnecessary exploration, we put forward the remaining utility reducing strategy and three other revised pruning strategies. Experiments on various datasets demonstrated that the designed IDHUP algorithm has the best performance in terms of running time compared to state-of-the-art algorithms.
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起訖頁 | 135-147 |
關鍵詞 | Pattern discovery、incremental mining、utility mining、dynamic data |
刊名 | 網際網路技術學刊 |
期數 | 202301 (24:1期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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