TASC: Two-attribute-set clustering through decision tree constructionClustering 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.
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Habermehl, Peter; Iosif, Radu; Vojnar, Tomáš: Automata-based verification of programs with tree updates (2010)
- Chen, Yen-Liang; Hsu, Wu-Hsien; Lee, Yu-Hsuan: TASC: two-attribute-set clustering through decision tree construction (2006)
- Flasiński, Mariusz (ed.); Nawarecki, Edward (ed.); Polkpwski, Lech (ed.); Schaefer, Robert (ed.); Stefanowski, Jerzy (ed.); Suraj, Zbigniew (ed.): Special issue: Theory and applications of soft computing (TASC 04). Selected, revised and expanded papers based on the presentations at theworkshop, Warsaw, Poland, November 2004 (2006)
- Habermehl, Peter; Iosif, Radu; Vojnar, Tomas: Automata-based verification of programs with tree updates (2006)