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JACIII Vol.11 No.6 pp. 554-560
doi: 10.20965/jaciii.2007.p0554
(2007)

Paper:

Unsupervised and Semi-Supervised Graph-Spectral Algorithms for Robust Extraction of Arbitrarily Shaped Fuzzy Clusters

Weiwei Du and Kiichi Urahama

Department of Visual Communication Design, Kyushu University, 4-9-1 Shiobaru, Fukuoka 815-8540, Japan

Received:
December 2, 2006
Accepted:
March 19, 2007
Published:
July 20, 2007
Keywords:
unsupervised clustering, semi-supervised clustering, graph-spectral algorithm, fuzzy clustering, image retrieval
Abstract

We present unsupervised and semi-supervised algorithms for extracting fuzzy clusters in weighted undirected regular, undirected bipartite, and directed graphs. We derive the semi-supervised algorithms from the Lagrangian function in unsupervised methods for extracting dominant clusters in a graph. These algorithms are robust against noisy data and extract arbitrarily shaped clusters. We demonstrate applications for similarity searches of data such as image retrieval in face images represented by undirected graphs, quantized color images represented by undirected bipartite graphs, and Web page links represented by directed graphs.

Cite this article as:
Weiwei Du and Kiichi Urahama, “Unsupervised and Semi-Supervised Graph-Spectral Algorithms for Robust Extraction of Arbitrarily Shaped Fuzzy Clusters,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.6, pp. 554-560, 2007.
Data files:
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