World's Best Scientists 2026 revealed!
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Computer Science
New Zealand
2025

D-Index & Metrics

Computer Science

D-Index
88
Citations
163678
World Ranking
652
National Ranking
1

Research.com Recognitions

  • 2025 - Research.com Computer Science in New Zealand Leader Award
  • 2023 - Research.com Computer Science in New Zealand Leader Award
  • 2022 - Research.com Computer Science in New Zealand Leader Award
  • 1997 - Fellow of the Royal Society of New Zealand
  • 1996 - ACM Fellow For contributions to the study of how past behavior can expedite future interaction, in particular adaptive data compression, programming by demonstration, and machine learning.

Overview

Ian H. Witten was a researcher affiliated with the University of Waikato in New Zealand. Their academic career involved contributing to multiple fields of study, primarily within computer science, social sciences, and the arts and humanities.

Their main areas of research included artificial intelligence, information systems, sociology and political science, computer science applications, and literature and literary theory. The variety of these subfields reflected a multidisciplinary approach to their scholarly work.

Among the specific topics Ian H. Witten worked on were natural language processing techniques, misinformation and its impacts, information retrieval and search behavior, web visibility and informetrics, online learning and analytics, text readability and simplification, and digital humanities and scholarship.

Their publication record featured contributions to Research Commons at the University of Waikato. Notably, one of their recent papers was titled "Searching... in a Web", published in 2020 by Research Commons (University of Waikato), which has received citations within the academic community.

  • Christopher Pal
  • James R. Foulds
  • Eibe Frank
  • Mark A. Hall

Throughout their career, Ian H. Witten published multiple works in the Research Commons at the University of Waikato and collaborated frequently with a set of co-authors. These collaborations included researchers such as Christopher Pal, James R. Foulds, Eibe Frank, and Mark A. Hall.

The researcher received formal recognition in their field, being named a Fellow of the Royal Society of New Zealand in 1997. They were also designated an ACM Fellow in 1996 for their contributions to the study of behavioral patterns facilitating future interactions, particularly in adaptive data compression, programming by demonstration, and machine learning.

Best Publications

  • Data mining: practical machine learning tools and techniques with Java implementations

    Ian H. Witten;Eibe Frank

  • Data Mining: Practical Machine Learning Tools and Techniques

    Ian H. Witten;Eibe Frank;Mark A. Hall

  • The WEKA data mining software: an update

    Mark Hall;Eibe Frank;Geoffrey Holmes;Bernhard Pfahringer

  • Arithmetic coding for data compression

    Ian H. Witten;Radford M. Neal;John G. Cleary

  • Managing Gigabytes: Compressing and Indexing Documents and Images

    I.H. Witten;A. Moffat;T.C. Bell

  • Text Compression

    Timothy C. Bell;John G. Cleary;Ian H. Witten

  • Generating Accurate Rule Sets Without Global Optimization

    Eibe Frank;Ian H. Witten

  • Data Compression Using Adaptive Coding and Partial String Matching

    J. Cleary;I. Witten

  • Learning to link with wikipedia

    David Milne;Ian H. Witten

  • Induction of model trees for predicting continuous classes

    Yong Wang;Ian H. Witten

  • WEKA: a machine learning workbench

    G. Holmes;A. Donkin;I.H. Witten

  • KEA: practical automatic keyphrase extraction

    Ian H. Witten;Gordon W. Paynter;Eibe Frank;Carl Gutwin

  • Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

    Ian H. Witten;Eibe Frank

  • Data mining in bioinformatics using Weka

    Eibe Frank;Mark Hall;Len Trigg;Geoffrey Holmes

  • Managing gigabytes (2nd ed.): compressing and indexing documents and images

    Ian H. Witten;Alistair Moffat;Timothy C. Bell

  • The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression

    I.H. Witten;T.C. Bell

  • An effective, low-cost measure of semantic relatedness obtained from Wikipedia links

    David Milne;Ian H. Witten

  • Issues in stacked generalization

    Kai Ming Ting;Ian H. Witten

  • Domain-specific keyphrase extraction

    Eibe Frank;Gordon W. Paynter;Ian H. Witten;Carl Gutwin

  • Weka: Practical machine learning tools and techniques with Java implementations

    Ian H. Witten;Eibe Frank;Leonard E. Trigg;Mark A. Hall

  • Arithmetic coding revisited

    Alistair Moffat;Radford M. Neal;Ian H. Witten

Frequent Co-Authors

Eibe Frank
Eibe Frank University of Waikato
Tim Bell
Tim Bell University of Canterbury
Alistair Moffat
Alistair Moffat University of Melbourne
Saul Greenberg
Saul Greenberg University of Calgary
Harold Thimbleby
Harold Thimbleby Swansea University
Chris Pal
Chris Pal Polytechnique Montréal
Marco Gori
Marco Gori University of Siena
Geoffrey Holmes
Geoffrey Holmes University of Waikato
Matt Jones
Matt Jones Swansea University
Radford M. Neal
Radford M. Neal University of Toronto

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