Journeys to Data Mining

Experiences from 15 Renowned Researchers

  • Mohamed Medhat Gaber

Table of contents

  1. Front Matter
    Pages i-viii
  2. Mohamed Medhat Gaber
    Pages 1-11
  3. Dean Abbott
    Pages 13-25
  4. Charu C. Aggarwal
    Pages 27-42
  5. Michael R. Berthold
    Pages 43-49
  6. Chris Clifton
    Pages 51-59
  7. David J. Hand
    Pages 77-91
  8. Cheryl G. Howard
    Pages 93-100
  9. Colleen McLaughlin McCue
    Pages 131-146
  10. Gregory Piatetsky-Shapiro
    Pages 173-196
  11. Graham J. Williams
    Pages 211-229
  12. Mohammed J. Zaki
    Pages 231-241

About this book

Introduction

Data mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing.

The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions:

1. What are your motives for conducting research in the data mining field?
2. Describe the milestones of your research in this field.
3. What are your notable success stories?
4. How did you learn from your failures?
5. Have you encountered unexpected results?
6. What are the current research issues and challenges in your area?
7. Describe your research tools and techniques.
8. How would you advise a young researcher to make an impact?
9. What do you predict for the next two years in your area?
10. What are your expectations in the long term?

In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offer.

Keywords

artificial intelligence careers in computer science data mining history of computing knowledge discovery in databases machine learning

Editors and affiliations

  • Mohamed Medhat Gaber
    • 1
  1. 1., School of ComputingUniversity of PortsmouthPortsmouthUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-28047-4
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-28046-7
  • Online ISBN 978-3-642-28047-4
  • About this book