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Predictive Clustering

  • Book

Overview

Authors:
  1. Hendrik Blockeel

    1. Katholieke Universiteit Leuven, Heverlee, Belgium

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  2. Sašo Džeroski

    1. Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

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  3. Jan Struyf

    1. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

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  4. Bernard Zenko

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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  • Features a new data mining approach: predictive clustering trees and rules
  • Presents a higher efficiency of the learning and prediction process
  • Provides straightforward content in an easy-to-understand manner

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Keywords

  • Data mining
  • Machine learning
  • Predictive clustering
  • analysis of remote sensing data
  • bio-informatics
  • classification
  • clustering
  • clustering time-series data
  • eco-informatics
  • ecological modeling
  • environmental quality classification
  • gene expression data analysis
  • habitat modeling
  • hierarchical classification
  • learning algorithms
  • multi-label classification
  • multi-target learning
  • multi-task learning
  • predicting gene function
  • regression
  • rule learning
  • species distribution
  • structured output prediction
  • tree learning

About this book

This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.

Authors and Affiliations

  • Katholieke Universiteit Leuven, Heverlee, Belgium

    Hendrik Blockeel

  • Department of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

  • Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Jan Struyf

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Bernard Zenko

Bibliographic Information

  • Book Title: Predictive Clustering

  • Authors: Hendrik Blockeel, Sašo Džeroski, Jan Struyf, Bernard Zenko

  • Publisher: Springer New York, NY

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2025

  • Hardcover ISBN: 978-1-4614-1146-8

  • eBook ISBN: 978-1-4614-1147-5

  • Edition Number: 1

  • Number of Pages: V, 240

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