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Towards utility-driven contiguous sequential patterns in uncertain multi-sequences
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AI
DOI：10.1016/j.knosys.2023.111314.png)
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Abstract
En 中文
In data mining, high -utility sequential pattern mining (HUSPM) focuses more on the specific values of items than on their frequency, making it more practical in real -life scenarios. HUSPM with the contiguous constraint can be used to solve some applications requiring the sequence elements to occur consecutively. Due to device, environment, privacy issues, and other factors, the data is often not accurate, and traditional algorithms for mining high -utility continuous sequence patterns (HUCSPs) do not perform well in handling uncertain data. To address this challenge, this paper presents a new algorithm named uncertain utility -driven contiguous pattern mining (UUCPM), which is able to discover HUCSPs in the uncertain database efficiently and correctly. The algorithm is designed to obtain results from sequence data with uncertain probability set on the item or itemset level. Two tighter upper bounds on utility and corresponding pruning strategies are also proposed, which can effectively process and reduce the number of candidate patterns generated during pattern mining, thereby improving the performance of the mining process. The proposed UUCPM algorithm has been verified for accuracy and performance through extensive experiments, demonstrating its advanced properties. The source code and datasets are available at GitHub https://github.com/DSI-Lab1/UUCPM.
Keywords:
Pattern discovery
Contiguous sequence
Sequential pattern
Uncertainty
Pruning strategies
Journal
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7.6
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1.2W
Citations:
4.5W
