Interactive Knowledge Discovery and Data Mining in Biomedical Informatics
State-of-the-Art and Future Challenges
Herausgeber: Holzinger, Andreas, Jurisica, Igor (Eds.)
Vorschau- 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 order to provide a research agenda to stimulate further research and progress
- Written by professionals from diverse areas with various backgrounds who share a common vision: making sense of complex data
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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; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.
- Inhaltsverzeichnis (19 Kapitel)
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Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions
Seiten 1-18
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Visual Data Mining: Effective Exploration of the Biological Universe
Seiten 19-33
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Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining
Seiten 35-56
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On the Generation of Point Cloud Data Sets: Step One in the Knowledge Discovery Process
Seiten 57-80
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Adapted Features and Instance Selection for Improving Co-training
Seiten 81-100
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Inhaltsverzeichnis (19 Kapitel)
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Bibliografische Information
- Bibliographic Information
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- Buchtitel
- Interactive Knowledge Discovery and Data Mining in Biomedical Informatics
- Buchuntertitel
- State-of-the-Art and Future Challenges
- Herausgeber
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- Andreas Holzinger
- Igor Jurisica
- Titel der Buchreihe
- Information Systems and Applications, incl. Internet/Web, and HCI
- Buchreihen Band
- 8401
- Copyright
- 2014
- Verlag
- Springer-Verlag Berlin Heidelberg
- Copyright Inhaber
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-662-43968-5
- DOI
- 10.1007/978-3-662-43968-5
- Softcover ISBN
- 978-3-662-43967-8
- Auflage
- 1
- Seitenzahl
- XX, 357
- Anzahl der Bilder
- 56 schwarz-weiß Abbildungen
- Themen
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