TASC: Two-attribute-set clustering through decision tree construction

Yen Liang Chen, Wu Hsien Hsu, Yu Hsuan Lee

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Clustering is the process of grouping a set of objects into classes of similar objects. In the past, clustering algorithms had a common problem that they use only one set of attributes for both partitioning the data space and measuring the similarity between objects. This problem has limited the use of the existing algorithms on some practical situation. Hence, this paper introduces a new clustering algorithm, which partitions data space by constructing a decision tree using one attribute set, and measures the degree of similarity using another. Three different partitioning methods are presented. The algorithm is explained with illustration. The performance and accuracy of the four partitioning methods are evaluated and compared.

Original languageEnglish
Pages (from-to)930-944
Number of pages15
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - 16 Oct 2006


  • Clustering
  • Data mining
  • Decision tree


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