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Titlebook: Visual Data Mining; Theory, Techniques a Simeon J. Simoff,Michael H. B?hlen,Arturas Mazeika Book 2008 Springer-Verlag Berlin Heidelberg 200

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書目名稱Visual Data Mining
副標題Theory, Techniques a
編輯Simeon J. Simoff,Michael H. B?hlen,Arturas Mazeika
視頻videohttp://file.papertrans.cn/984/983711/983711.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Visual Data Mining; Theory, Techniques a Simeon J. Simoff,Michael H. B?hlen,Arturas Mazeika Book 2008 Springer-Verlag Berlin Heidelberg 200
描述.The importance of visual data mining, as a strong sub-discipline of data mining, had already been recognized in the beginning of the decade. In 2005 a panel of renowned individuals met to address the shortcomings and drawbacks of the current state of visual information processing. The need for a systematic and methodological development of visual analytics was detected...This book aims at addressing this need. Through a collection of 21 contributions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications..
出版日期Book 2008
關(guān)鍵詞SVM classifiers; association rules mining; clusterin; context visualizations; data mining; decision trees
版次1
doihttps://doi.org/10.1007/978-3-540-71080-6
isbn_ebook978-3-540-71080-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2008
The information of publication is updating

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Visual Analytics: Scope and Challengeslytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.
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Complementing Visual Data Mining with the Sound Dimension: Sonification of Time Dependent Datament via a dedicated server. The second one was based on the analysis of an evaluation questionnaire, filled by each participant that performed the tests. The chapter presents the results and the issues raised by the experiments.
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Context Visualization for Visual Data Miningtion techniques supporting visual data mining in general as well as for visual preservation of context and history information. Algorithms are also described in supporting such methodology for visual data mining in real time.
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Book 2008tions selected from more than 46 submissions, it offers a systematic presentation of the state of the art in the field. The volume is structured in three parts on theory and methodologies, techniques, and tools and applications..
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