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Titlebook: New Frontiers in Mining Complex Patterns; 4th International Wo Michelangelo Ceci,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 20

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书目名称New Frontiers in Mining Complex Patterns
副标题4th International Wo
编辑Michelangelo Ceci,Corrado Loglisci,Zbigniew W. Ras
视频video
概述Includes supplementary material:
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: New Frontiers in Mining Complex Patterns; 4th International Wo Michelangelo Ceci,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 20
描述.This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2015, held in conjunction with ECML-PKDD 2015 in Porto, Portugal, in September 2015...The 15 revised full papers presented together with  one invited talk were carefully reviewed and selected from 19 submissions. Theyillustrate 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: data stream mining, classification, mining complex data, and sequences..
出版日期Conference proceedings 2016
关键词data mining; ensemble methods; knowledge discovery; machine learning; parallel algorithms; classification
版次1
doihttps://doi.org/10.1007/978-3-319-39315-5
isbn_softcover978-3-319-39314-8
isbn_ebook978-3-319-39315-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Frequent Itemsets Mining in Data Streams Using Reconfigurable Hardware approaches for Data Mining cannot be used straightforwardly in data stream scenario. This paper introduces a single-pass hardware-based algorithm for frequent itemsets mining on data streams that uses the top-k frequent 1-itemsets. Experimental results of the hardware implementation of the proposed
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The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data the most accurate among them. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers. However, they are less diversified than components of the standard baggin
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Classifying Traces of Event Logs on the Basis of Security Riskse basis of some (possibly incomplete) knowledge of the process structures and of the patterns representing unsecure behaviors, we classify each trace as instance of some process and/or as potential security breach. This classification is addressed in the challenging setting where each event has not
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