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Identifying Single Good Clusters in Data Sets.
- Source :
- Advances in Machine Vision, Image Processing, & Pattern Analysis; 2006, p160-167, 8p
- Publication Year :
- 2006
-
Abstract
- Local patterns in the form of single clusters are of interest in various areas of data mining. However, since the intention of cluster analysis is a global partition of a data set into clusters, it is not suitable to identify single clusters in a large data set where the majority of the data can not be assigned to meaningful clusters. This paper presents a new objective function-based approach to identify a single good cluster in a data set making use of techniques known from prototype-based, noise and fuzzy clustering. The proposed method can either be applied in order to identify single clusters or to carry out a standard cluster analysis by finding clusters step by step and determining the number of clusters automatically in this way. Keywords: Cluster analysis, local pattern discovery. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540375975
- Database :
- Complementary Index
- Journal :
- Advances in Machine Vision, Image Processing, & Pattern Analysis
- Publication Type :
- Book
- Accession number :
- 32961818
- Full Text :
- https://doi.org/10.1007/11821045_17