标题:Efficient mining of pareto-front high expected utility patterns
作者:Ahmed, Usman ;Lin, Jerry Chun-Wei ;Wu, Jimmy Ming-Tai ;Djenouri, Youcef ;Srivastava, Gautam ;Mukhiya, Suresh Kumar
通讯作者:Lin, Jerry ChunWei
作者机构:[Ahmed, Usman ;Lin, Jerry Chun-Wei ;Mukhiya, Suresh Kumar ] Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western 更多
会议名称:33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
会议日期:22 September 2020 through 25 September 2020
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2020
卷:12144 LNAI
页码:872-883
DOI:10.1007/978-3-030-55789-8_74
关键词:Data mining; Evolutionary computation; High expected utility pattern mining; Multi-objective optimization; Uncertain databases
摘要:In this paper, we present a model called MHEUPM to efficiently mine the interesting high expected utility patterns (HEUPs) by employing the multi-objective evolutionary framework. The model considers both uncertainty and utility factors to discover meaningful HEUPMs without requiring pre-defined threshold values (such as minimum utility and minimum uncertainty). The effectiveness of the model is validated using two encoding methodologies. The proposed MHEUPM model can discover a set of HEUPs within a limited period. The efficiency of the proposed model is determined through rigorous analysis and compared to the standard pattern-mining methods in terms of hypervolume, convergence, and number of the discovered patterns. © Springer Nature Switzerland AG 2020.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091297565&doi=10.1007%2f978-3-030-55789-8_74&partnerID=40&md5=1faede9833f4d4d8a34eec36059e8b69
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