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Improving the Robustness of Online Agglomerative Clustering Method Based on Kernel-Induce Distance Measures.
- Source :
- Neural Processing Letters; Feb2005, Vol. 21 Issue 1, p45-51, 7p
- Publication Year :
- 2005
-
Abstract
- Abstract Recently, an online agglomerative clustering method called AddC (I. D. Guedalia et al . Neural Comput . {\bf 11} (1999), 521--540) was proposed for nonstationary data clustering. Although AddC possesses many good attributes, a vital problem of that method is its sensitivity to noises, which limits its use in real-word applications. In this paper, based on \hbox{kernel-induced} distance measures, a robust online clustering (ROC) algorithm is proposed to remedy the problem of AddC. Experimental results on artificial and benchmark data sets show that ROC has better clustering performances than AddC, while still inheriting advantages such as clustering data in a single pass and without knowing the exact number of clusters beforehand. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
ALGEBRA
FOUNDATIONS of arithmetic
MATHEMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 13704621
- Volume :
- 21
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Neural Processing Letters
- Publication Type :
- Academic Journal
- Accession number :
- 18425304