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Clustering Orders

  • Toshihiro Kamishima
  • Jun Fujiki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2843)

Abstract

We propose a method of using clustering techniques to partition a set of orders. We define the term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. These orders are useful for, say, carrying out a sensory survey. We propose a method called the k-o’means method, which is a modified version of a k-means method, adjusted to handle orders. We compared our method with the traditional clustering methods, and analyzed its characteristics. We also applied our method to a questionnaire survey data on people’s preferences in types of sushi (a Japanese food).

Keywords

Comparative Judgment Object Pair Sample Order Cluster Order Generalize Order Statistic 
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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Toshihiro Kamishima
    • 1
  • Jun Fujiki
    • 1
  1. 1.AIST Tsukuba Central 2IbarakiJapan

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