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An Evaluation of Sampling Methods for Data Mining with Fuzzy C-Means

  • K. Josien
  • G. Wang
  • T. W. Liao
  • E. Triantaphyllou
  • M. C. Liu
Part of the Massive Computing book series (MACO, volume 3)

Abstract

Using fuzzy c-means as the data-mining tool, this study evaluates the effectiveness of sampling methods in producing the knowledge of interest. The effectiveness is shown in terms of the representative-ness of sampling data and both the accuracy and errors of sampled data sets when subjected to the fuzzy clustering algorithm. Two population data in the weld inspection domain were used for the evaluation. Based on the results obtained, a number of observations are made.

Keywords

Data Mining False Positive Rate False Negative Rate Fuzzy Cluster Algorithm Statistical Test Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • K. Josien
    • 1
  • G. Wang
    • 1
  • T. W. Liao
    • 1
  • E. Triantaphyllou
    • 1
  • M. C. Liu
    • 2
  1. 1.Industrial & Manufacturing Systems Engineering DepartmentLouisiana State UniversityBaton RougeUSA
  2. 2.Manufacturing R&DBoeing CompanyWichitaUSA

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