Data Mining Applications Using Artificial Adaptive Systems

  • William J.┬áTastle

Table of contents

  1. Front Matter
    Pages i-vii
  2. Massimo Buscema, Francis Newman, Giulia Massini, Enzo Grossi, William J. Tastle, Arthur K. Liu
    Pages 1-23
  3. Massimo Buscema, Roberto Passariello, Enzo Grossi, Giulia Massini, Francesco Fraioli, Goffredo Serra
    Pages 25-61
  4. Giulia Massini, Stefano Terzi, Massimo Buscema
    Pages 63-74
  5. Massimo Buscema, Marco Breda, Enzo Grossi, Luigi Catzola, Pier Luigi Sacco
    Pages 75-139
  6. Massimo Buscema, William J. Tastle, Stefano Terzi
    Pages 141-182
  7. Massimo Buscema, Pier Luigi Sacco
    Pages 183-209
  8. Massimo Buscema, Pier Luigi Sacco
    Pages 211-230
  9. Massimo Buscema, Pier Luigi Sacco, Enzo Grossi, Weldon A. Lodwick
    Pages 231-275

About this book


This volume directly addresses the complexities involved in data mining and the development of new algorithms, built on an underlying theory consisting of linear and non-linear dynamics, data selection, filtering, and analysis, while including analytical projection and prediction. The results derived from the analysis are then further manipulated such that a visual representation is derived with an accompanying analysis. The book brings very current methods of analysis to the forefront of the discipline, provides researchers and practitioners the mathematical underpinning of the algorithms, and the non-specialist with a visual representation such that a valid understanding of the meaning of the adaptive system can be attained with careful attention to the visual representation. The book presents, as a collection of documents, sophisticated and meaningful methods that can be immediately understood and applied to various other disciplines of research. The content is composed of chapters addressing: An application of adaptive systems methodology in the field of post-radiation treatment involving brain volume differences in children; A new adaptive system for computer-aided diagnosis of the characterization of lung nodules; A new method of multi-dimensional scaling with minimal loss of information; A description of the semantics of point spaces with an application on the analysis of terrorist attacks in Afghanistan; The description of a new family of meta-classifiers; A new method of optimal informational sorting; A general method for the unsupervised adaptive classification for learning; and the presentation of two new theories, one in target diffusion and the other in twisting theory.


Data Mining Spatiotemporal mining Unsupervised learning adaptive systems linear dynamics multi-dimensional scaling non-linear dynamics target diffusion twisting theory

Editors and affiliations

  • William J.┬áTastle
    • 1
  1. 1.Ithaca CollegeIthacaUSA

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4614-4222-6
  • Online ISBN 978-1-4614-4223-3
  • Buy this book on publisher's site