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Titlebook: Complex Pattern Mining; New Challenges, Meth Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Book 2020 Springer Nature Switzerland AG 202

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发表于 2025-3-21 19:11:17 | 显示全部楼层 |阅读模式
书目名称Complex Pattern Mining
副标题New Challenges, Meth
编辑Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras
视频videohttp://file.papertrans.cn/232/231520/231520.mp4
概述Presents recent research in complex pattern mining.Includes revised selected papers presented at the workshops on "New Frontiers in Mining Complex Patterns”.Presents new challenges, methods and applic
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Complex Pattern Mining; New Challenges, Meth Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Book 2020 Springer Nature Switzerland AG 202
描述This book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data now gathered in various real-world applications (e.g., business process monitoring, cybersecurity, medicine, language processing, and remote sensing). The book consists of 14 chapters covering the latest research by the authors and the research centers they represent. It illustrates techniques and algorithms that have recently been developed to preserve the richness of the data and allow us to efficiently and effectively identify the complex information it contains. Presenting the latest developments in complex pattern mining, this book is a valuable reference resource for data science researchers and professionals in academia and industry.
出版日期Book 2020
关键词Complex Pattern Mining; NFMCP; Representation Formalisms; Pattern Discovery; Foundations of Pattern Mini
版次1
doihttps://doi.org/10.1007/978-3-030-36617-9
isbn_softcover978-3-030-36619-3
isbn_ebook978-3-030-36617-9Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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A Text-Based Regression Approach to Predict Bug-Fix Time,M5P model tree, Support Vector Machine (SVM) and Random Forests algorithms. Experimental results show the model is effective, in fact, they are slightly better than all the ones known in the literature. In the future, we will use and compare other different regression approaches to select the best one for a specific data set.
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A Latitudinal Study on the Use of Sequential and Concurrency Patterns in Deviance Mining,l and concurrency patterns is performed through experiments on two real-world event logs, by varying both classification and feature extraction algorithms. Our results show that the pattern representation has limited impact on classification performance, while the use of concurrency patterns provides more meaningful insights on deviant behavior.
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