Buch, Englisch, Band 8401, 357 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 5737 g
Reihe: Information Systems and Applications, incl. Internet/Web, and HCI
State-of-the-Art and Future Challenges
Buch, Englisch, Band 8401, 357 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 5737 g
Reihe: Information Systems and Applications, incl. Internet/Web, and HCI
ISBN: 978-3-662-43967-8
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
Weitere Infos & Material
Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions.- Visual Data Mining: Effective Exploration of the Biological Universe.- Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining.- On the Generation of Point Cloud Data Sets: Step One in the Knowledge Discovery Process.- Adapted Features and Instance Selection for Improving Co-training.- Knowledge Discovery and Visualization of Clusters for Erythromycin Related Adverse Events in the FDA Drug Adverse Event Reporting System.- On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics.- A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data.- Interactive Data Exploration Using Pattern Mining.- Resources for Studying Statistical Analysis of Biomedical Data and R.- A Kernel-Based Framework for Medical Big-Data Analytics.- On Entropy-Based Data Mining.- Sparse Inverse Covariance Estimation for Graph Representation of Feature Structure.- Multi-touch Graph-Based Interaction for Knowledge Discovery on Mobile Devices: State-of-the-Art and Future Challenges.- Intelligent Integrative Knowledge Bases: Bridging Genomics, Integrative Biology and Translational Medicine.- Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges.- Protecting Anonymity in Data-Driven Biomedical Science.- Biobanks – A Source of Large Biological Data Sets: Open Problems and Future Challenges.- On Topological Data Mining.