Clustering Through Decision Tree Construction

  title={Clustering Through Decision Tree Construction},
  author={Bing Liu and Yiyuan Xia and Philip S. Yu},
Clustering aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. It is often called unsupervised learning as no class labels denoting an a priori partition of the objects are given. This is in contrast with supervised learning (e.g., classification) for which the data objects are already labeled with known classes. Past research in clustering has produced many algorithms. However, these algorithms have some major shortcomings. In this paper… CONTINUE READING
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CLTree - Clustering through decision tree construction.

  • B. Liu, Y. Xia, P. S. Yu
  • Forthcoming, an earlier version appeared as an…
  • 2000
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7 Excerpts

C4.5: program for machine learning

  • J. R. Quinlan
  • 1992
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10 Excerpts

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