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CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction
Date
2016-05-20
Author
Alkan, Oznur Kirmemis
Karagöz, Pınar
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This paper presents efficient data structures and a pruning technique in order to improve the efficiency of high utility sequential pattern mining. CRoM (Cumulated Rest of Match) based upper bound, which is a tight upper bound on the utility of the candidates is proposed in order to perform more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, an efficient algorithm, HuspExt (High Utility Sequential Pattern Extraction), is presented which calculates the utilities of the child patterns based on that of the parents'. Substantial experiments on both synthetic and real datasets from different domains show that, the solution efficiently discovers high utility sequential patterns under low thresholds.
URI
https://hdl.handle.net/11511/55819
Conference Name
32nd IEEE International Conference on Data Engineering (ICDE)
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Department of Computer Engineering, Conference / Seminar
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This paper presents efficient data structures and a pruning technique in order to improve the efficiency of high utility sequential pattern mining. CRoM (Cumulated Rest of Match) based upper bound, which is a tight upper bound on the utility of the candidates is proposed in order to perform more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, an efficient algorithm, HuspExt (High Utility Sequential Pattern Extraction), is presented which calcul...
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O. K. Alkan and P. Karagöz, “CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction,” presented at the 32nd IEEE International Conference on Data Engineering (ICDE), Helsinki, FINLAND, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55819.
