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Titlebook: Emerging Trends in Knowledge Discovery and Data Mining; PAKDD 2012 Internati Takashi Washio,Jun Luo Conference proceedings 2013 Springer-Ve

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發(fā)表于 2025-3-21 17:33:36 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Emerging Trends in Knowledge Discovery and Data Mining
副標題PAKDD 2012 Internati
編輯Takashi Washio,Jun Luo
視頻videohttp://file.papertrans.cn/309/308516/308516.mp4
概述High quality selected papers.Unique visibility.State of the art research
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Emerging Trends in Knowledge Discovery and Data Mining; PAKDD 2012 Internati Takashi Washio,Jun Luo Conference proceedings 2013 Springer-Ve
描述This book constitutes the thoroughly refereed proceedings of the PAKDD 2012 International Workshops: Third Workshop on Data Mining for Healthcare Management (DMHM 2012), First Workshop on Geospatial Information and Documents (GeoDoc 2012), First Workshop on Multi-view data, High-dimensionality, External Knowledge: Striving for a Unified Approach to Clustering (3Clust 2012), and the Second Doctoral Symposium on Data Mining (DSDM 2012); held in conjunction with the 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), in Kuala Lumpur, Malaysia, May/June 2012. .The 12 revised papers presented were carefully reviewed and selected from numerous submissions. DMHM 2012 aimed at providing a common platform for the discussion of challenging issues and potential techniques in this emerging field of data mining for health care management; 3Clust 2012 focused on solving emerging problems such as clustering ensembles, semi-supervised clustering, subspace/projective clustering, co-clustering, and multi-view clustering; GeoDoc 2012 highlighted the formalization of geospatial concepts and relationships with a focus on the extraction of geospatial relations in free text
出版日期Conference proceedings 2013
關鍵詞classification; health informatics; information retrieval; machine learning; process mining
版次1
doihttps://doi.org/10.1007/978-3-642-36778-6
isbn_softcover978-3-642-36777-9
isbn_ebook978-3-642-36778-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2013
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,Adaptive Evidence Accumulation Clustering Using the Confidence of the Objects’ Assignments, cluster. The degree of confidence is then used to select which objects should be emphasized in the learning process of the clustering algorithm. New consensus partition validity measures, based on the notion of degree of confidence, are also proposed. In order to evaluate the performance of our app
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0302-9743 ering, subspace/projective clustering, co-clustering, and multi-view clustering; GeoDoc 2012 highlighted the formalization of geospatial concepts and relationships with a focus on the extraction of geospatial relations in free text 978-3-642-36777-9978-3-642-36778-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Offenlegungspolitik von Investmentfonds cluster. The degree of confidence is then used to select which objects should be emphasized in the learning process of the clustering algorithm. New consensus partition validity measures, based on the notion of degree of confidence, are also proposed. In order to evaluate the performance of our app
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