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Mining high utility pattern with negative items in dynamic databases
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DOI：10.1002/int.22794.png)
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High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.
Keyword:
high utility pattern mining
index
negative utility
sliding window
utility list
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3.7
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3.1K
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8.1K
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Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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分享收藏Incremental high utility pattern mining with static and dynamic databases基于静态和动态数据库的增量式高效用模式挖掘APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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