© 2001

Instance Selection and Construction for Data Mining

  • Huan Liu
  • Hiroshi Motoda

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Background and Foundation

    1. Front Matter
      Pages 1-1
    2. Huan Liu, Hiroshi Motoda
      Pages 3-20
    3. Baohua Gu, Feifang Hu, Huan Liu
      Pages 21-38
    4. Thomas Reinartz
      Pages 39-56
  3. Instance Selection Methods

    1. Front Matter
      Pages 57-57
    2. Barry Smyth, Elizabeth McKenna
      Pages 59-76
    3. Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii
      Pages 95-112
    4. Chang-Shing Perng, Sylvia R. Zhang, D. Stott Parker
      Pages 113-130
  4. Use of Sampling Methods

    1. Front Matter
      Pages 131-131
    2. Carlos Domingo, Ricard Gavaldà, Osamu Watanabe
      Pages 133-150
    3. Foster Provost, David Jensen, Tim Oates
      Pages 151-170
    4. Hankil Yoon, Khaled Alsabti, Sanjay Ranka
      Pages 189-206
  5. Unconventional Methods

    1. Front Matter
      Pages 207-207
    2. David Madigan, Nandini Raghavan, William DuMouchel, Martha Nason, Christian Posse, Greg Ridgeway
      Pages 209-226
    3. Wai Lam, Chi-Kin Keung, Charles X. Ling
      Pages 227-244
    4. Peggy Wright, Julia Hodges
      Pages 263-279

About this book


The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency.
One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances - data points - for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc.
Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.


DOM Time series algorithms case-based reasoning classification data mining database filtering genetic algorithm genetic algorithms knowledge knowledge discovery learning

Editors and affiliations

  • Huan Liu
    • 1
  • Hiroshi Motoda
    • 2
  1. 1.Arizona State UniversityUSA
  2. 2.Osaka UniversityJapan

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