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Titlebook: New Frontiers in Mining Complex Patterns; Third International Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Conference proceedings 201

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发表于 2025-3-21 17:52:57 | 显示全部楼层 |阅读模式
书目名称New Frontiers in Mining Complex Patterns
副标题Third International
编辑Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras
视频video
概述Up-to-date results.Fast track conference proceedings.State-of-the-art report.Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: New Frontiers in Mining Complex Patterns; Third International  Annalisa Appice,Michelangelo Ceci,Zbigniew W. Ras Conference proceedings 201
描述This book constitutes the thoroughly refereed post-conference proceedings of the Third International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2014, held in conjunction with ECML-PKDD 2014 in Nancy, France, in September 2014.The 13 revised full papers presented were carefully reviewed and selected from numerous submissions. They illustrate advanced data mining techniques which preserve the informative richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: classification and regression; clustering; data streams and sequences; applications.
出版日期Conference proceedings 2015
关键词classification; clustering; data mining; feature selection; machine learning; network models; semantic sim
版次1
doihttps://doi.org/10.1007/978-3-319-17876-9
isbn_softcover978-3-319-17875-2
isbn_ebook978-3-319-17876-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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发表于 2025-3-21 23:19:44 | 显示全部楼层
New Frontiers in Mining Complex Patterns978-3-319-17876-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Mining Positional Data Streamsicable in our continuous setting. We propose an efficient trajectory-based preprocessing to identify similar movements and a distributed pattern mining algorithm to identify frequent trajectories. We empirically evaluate all parts of the processing pipeline.
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Semi-supervised Learning for Multi-target Regressioni-target regression (MTR), a type of structured output prediction, where the output space consists of multiple numerical values. Our main objective is to investigate whether we can improve over supervised methods for MTR by using unlabeled data. We use ensembles of predictive clustering trees in a s
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Evaluation of Different Data-Derived Label Hierarchies in Multi-label Classificationy using four different clustering algorithms (balanced .-means, agglomerative clustering with single and complete linkage and predictive clustering trees). The hierarchies are then used in conjunction with global hierarchical multi-label classification (HMC) approaches. The results from the statisti
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Predicting Negative Side Effects of Surgeries Through Clustering We propose a system that measures the similarity of a new patient to existing clusters, and makes a personalized decision on the patient’s most likely negative side effects. We further evaluate our system using SID, which is part of the Healthcare Cost and Utilization Project (HCUP). Our experiment
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