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Mining patterns for clustering on numerical datasets using unsupervised decision trees.
- Source :
-
Knowledge-Based Systems . Jul2015, Vol. 82, p70-79. 10p. - Publication Year :
- 2015
-
Abstract
- Pattern-based clustering algorithms return a set of patterns that describe the objects of each cluster. The most recent algorithms proposed in this approach extract patterns on numerical datasets by applying an a priori discretization process, which may cause information loss. In this paper, we introduce a new pattern-based clustering algorithm for numerical datasets, which does not need an a priori discretization on numerical features. The new algorithm extracts, from a collection of trees generated through a new induction procedure, a small subset of patterns useful for clustering. Experimental results show that the patterns extracted by the proposed algorithm allows to build a pattern-based clustering algorithm, which obtains better clustering results than recent pattern-based clustering algorithms. In addition, the proposed algorithm obtains similar clustering results, in quality, than traditional clustering algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 82
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 102217733
- Full Text :
- https://doi.org/10.1016/j.knosys.2015.02.019