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Data Mining: Foundations and Intelligent Paradigms

VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

  • Book
  • © 2012

Overview

Editors:
  1. Dawn E. Holmes

    1. Department of Statistics andApplied Probability, University of California , Santa Barbara, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Lakhmi C. Jain

    1. Knowledge-Based Engineering, University of South Australia, Adelaide Mawson Lakes, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • contains the latest research on data mining research and its applications to health informatics
  • The state of the art of data mining for health informatics is presented in a handbook style
  • Written by leading experts in this field

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 24)

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Table of contents (11 chapters)

  1. Front Matter

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  2. Advanced Modelling Paradigms in Data Mining

    • Dawn E. Holmes, Jeffrey Tweedale, Lakhmi C. Jain
    Pages 1-7

  3. Data Mining with Multilayer Perceptrons and Support Vector Machines

    • Paulo Cortez
    Pages 9-25

  4. Regulatory Networks under Ellipsoidal Uncertainty – Data Analysis and Prediction by Optimization Theory and Dynamical Systems

    • Erik Kropat, Gerhard-Wilhelm Weber, Chandra Sekhar Pedamallu
    Pages 27-56

  5. A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid

    • Eugenio Cesario, Marco Lackovic, Domenico Talia, Paolo Trunfio
    Pages 57-75

  6. Formal Framework for the Study of Algorithmic Properties of Objective Interestingness Measures

    • Yannick Le Bras, Philippe Lenca, Stéphane Lallich
    Pages 77-98

  7. Nonnegative Matrix Factorization: Models, Algorithms and Applications

    • Zhong-Yuan Zhang
    Pages 99-134

  8. Visual Data Mining and Discovery with Binarized Vectors

    • Boris Kovalerchuk, Florian Delizy, Logan Riggs, Evgenii Vityaev
    Pages 135-156

  9. A New Approach and Its Applications for Time Series Analysis and Prediction Based on Moving Average of n th-Order Difference

    • Yang Lan, Daniel Neagu
    Pages 157-182

  10. Exceptional Model Mining

    • Arno Knobbe, Ad Feelders, Dennis Leman
    Pages 183-198

  11. Online ChiMerge Algorithm

    • Petri Lehtinen, Matti Saarela, Tapio Elomaa
    Pages 199-216

  12. Mining Chains of Relations

    • Foto Aftrati, Gautam Das, Aristides Gionis, Heikki Mannila, Taneli Mielikäinen, Panayiotis Tsaparas
    Pages 217-246

  13. Back Matter

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Keywords

About this book

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Editors and Affiliations

  • Department of Statistics andApplied Probability, University of California , Santa Barbara, USA

    Dawn E. Holmes

  • Knowledge-Based Engineering, University of South Australia, Adelaide Mawson Lakes, Australia

    Lakhmi C. Jain

Bibliographic Information

  • Book Title: Data Mining: Foundations and Intelligent Paradigms

  • Book Subtitle: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects

  • Editors: Dawn E. Holmes, Lakhmi C. Jain

  • Series Title: Intelligent Systems Reference Library

  • DOI: https://doi.org/10.1007/978-3-642-23241-1

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2012

  • Hardcover ISBN: 978-3-642-23240-4Published: 07 November 2011

  • Softcover ISBN: 978-3-642-43429-7Published: 26 January 2014

  • eBook ISBN: 978-3-642-23241-1Published: 09 November 2011

  • Series ISSN: 1868-4394

  • Series E-ISSN: 1868-4408

  • Edition Number: 1

  • Number of Pages: XIV, 250

  • Topics: Computational Intelligence, Artificial Intelligence, Health Informatics

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