Skip to main content
  • Living reference work
  • © 2020

Encyclopedia of Machine Learning and Data Science

  • 800 entries covering key concepts and terms in the field of machine learning. Entries include in-depth essays and definitions, historical background, and key applications

  • Extensive cross-references support efficient, user-friendly searchers for immediate access to useful information

  • Serves to open the field to those inquiring into this fast-growing area of research

This is a preview of subscription content, access via your institution.

Table of contents (177 entries)

  1. K-Means Clustering

    • Xin Jin, Jiawei Han
  2. K-Medoids Clustering

    • Xin Jin, Jiawei Han
  3. K-Way Spectral Clustering

    • Xin Jin, Jiawei Han
  4. Abduction

    • Antonis C. Kakas
  5. Adaptive Resonance Theory

    • Gail A. Carpenter, Stephen Grossberg
  6. Adversarial Learning on Malware

    • Christopher Molloy, Ziad Mansour, Steven H. H. Ding
  7. Anomaly Detection

    • Varun Chandola, Arindam Banerjee, Vipin Kumar
  8. Anonymizing Trajectory Data

    • Khalil Al-Hussaeni
  9. Authorship Analysis with Machine Learning

    • Waqas Ahmed, Abdul Rehman Javed, Zunera Jalil, Farkhund Iqbal
  10. Autonomous Helicopter Flight Using Reinforcement Learning

    • Adam Coates, Pieter Abbeel, Andrew Y. Ng
  11. Bayes’ Rule

    • Geoffrey I Webb
  12. Bias-Variance Trade-offs: Novel Applications

    • Dev Rajnarayan, David Wolpert
  13. Biomedical Informatics

    • C. David Page, Sriraam Natarajan
  14. Boltzmann Machines

    • Geoffrey Hinton
  15. Cascade–Correlation

    • Thomas R. Shultz, Ardavan S. Nobandegani, Scott E. Fahlman
  16. Categorical Data Clustering

    • Periklis Andritsos, Panayiotis Tsaparas
  17. Causality

    • Ricardo Silva
  18. Class Binarization

    • Johannes Fürnkranz

About this book

This authoritative, expanded and updated third edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining.  A paramount work, its 1000 entries – over 200 of them newly updated or added --are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Science include recent developments in Deep Learning, Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others.  Topics were selected by a distinguished international advisory board.

Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.


Editors and Affiliations

  • Clayton, Australia

    Dinh Phung

  • Software Engineering, Monash University School of Computer Science &, Melbourne, Australia

    Geoffrey I. Webb

  • Engineering (CSE), University of New South Wales School of Computer Science &, Sydney, Australia

    Claude Sammut

About the editors

Claude Sammut is a Professor of Computer Science and Engineering at the University of New South Wales, Australia, and Head of the Artificial Intelligence Research Group. He is the UNSW node Director of the ARC Centre of Excellence for Autonomous Systems and a member of the joint ARC/NH&MRC project on Thinking Systems. He is on the editorial boards of the Journal of Machine Learning Research, the Machine Learning Journal and New Generation Computing, and was the chairman of the 2007 International Conference on Machine Learning.

Geoffrey I. Webb is research professor in the faculty of Information Technology at Monash University, Melbourne, Australia. He has published more than 150 scientific papers and is the author of the data mining software package Magnum Opus. His research areas include strategies for strengthening the Naïve Bayes machine learning technique, K-optimal pattern discovery, and work on Occam’s razor. He is editor-in-chief of Springer’s Data Mining and Knowledge Discovery journal, as well as being on the editorial board of Machine Learning.

Bibliographic Information