期刊
PATTERN RECOGNITION
卷 44, 期 7, 页码 1372-1386出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2011.01.003
关键词
Clustering; Normalized cuts; A contrario detection
资金
- CNES [DCT/SI/MO-2010.001.4673]
- FREEDOM [ANR07-JCJC-0048-01]
- Callisto [ANR-09-CORD-003]
- ECOS Sud [U06E01]
- ARFITEC [07 MATRH]
- Uruguayan Agency for Research and Innovation (ANII) [PR-POS-2008-003]
Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this work we present a new clustering method which is parameterless, independent from the original data dimensionality and from the shape of the clusters. It only takes into account inter-point distances and it has no random steps. The combination of the proposed method with normalized cuts proved successful in our experiments. (c) 2011 Elsevier Ltd. All rights reserved.
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