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Titlebook: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics; State-of-the-Art and Andreas Holzinger,Igor Jurisica Book 2014 S

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發(fā)表于 2025-3-21 17:13:40 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Interactive Knowledge Discovery and Data Mining in Biomedical Informatics
副標題State-of-the-Art and
編輯Andreas Holzinger,Igor Jurisica
視頻videohttp://file.papertrans.cn/471/470507/470507.mp4
概述Focuses on hot topics from interactive knowledge discovery and data mining in biomedical informatics.Each paper describes the state-of-the-art and focuses on open problems and future challenges in ord
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics; State-of-the-Art and Andreas Holzinger,Igor Jurisica Book 2014 S
描述.One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of <= 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning..This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Ar
出版日期Book 2014
關鍵詞big data; biomedical informatics; computing methodologies; data analytics; data mining; feature selection
版次1
doihttps://doi.org/10.1007/978-3-662-43968-5
isbn_softcover978-3-662-43967-8
isbn_ebook978-3-662-43968-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2014
The information of publication is updating

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