Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics: State-of-the-Art and Future Challenges

Paperback(2014)

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Product Details

ISBN-13: 9783662439678
Publisher: Springer Berlin Heidelberg
Publication date: 06/11/2014
Series: Lecture Notes in Computer Science , #8401
Edition description: 2014
Pages: 357
Product dimensions: 6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

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.

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