An improved cluster labeling method for support vector clustering

Author

Lee, Jaewook ; Lee, Daewon

Author_Institution

Dept. of Ind. Eng., Pohang Inst. of Sci. & Technol., South Korea

Volume

27

Issue

3

fYear

2005

fDate

3/1/2005 12:00:00 AM

Firstpage

461

Lastpage

464

Abstract

The support vector clustering (SVC) algorithm is a recently emerged unsupervised learning method inspired by support vector machines. One key step involved in the SVC algorithm is the cluster assignment of each data point. A new cluster labeling method for SVC is developed based on some invariant topological properties of a trained kernel radius function. Benchmark results show that the proposed method outperforms previously reported labeling techniques.

Keywords

pattern clustering; statistical analysis; support vector machines; topology; unsupervised learning; cluster assignment; cluster labeling method; kernel radius function; support vector clustering algorithm; support vector machines; topological properties; unsupervised learning method; Character generation; Clustering algorithms; Computer simulation; Kernel; Labeling; Robustness; Shape; Static VAr compensators; Support vector machines; Unsupervised learning; Index Terms- Clustering; support vector machines.; unsupervised learning method; Algorithms; Artificial Intelligence; Cluster Analysis; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2005.47

Filename

1388271