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Uncertain Data Mining: An Example in Clustering Location Data

  • Michael Chau
  • Reynold Cheng
  • Ben Kao
  • Jackey Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)

Abstract

Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Chau
    • 1
  • Reynold Cheng
    • 2
  • Ben Kao
    • 3
  • Jackey Ng
    • 1
  1. 1.School of BusinessThe University of Hong KongPokfulam, Hong KongChina
  2. 2.Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong
  3. 3.Department of Computer ScienceThe University of Hong KongPokfulamHong Kong

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