Paper:
Language: English:
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
Keywords: unsupervised clustering, semi-supervised clustering, graph-spectral algorithm, fuzzy clustering, image retrieval
Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.11, No.6 pp. 554-560, 2007
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.
Preview (PDF)
Reference
[Notice]
* "Preview" is the first 2 pages of the article. You don't need the registration.
* To read the PDF file you will then need to download and install the Adobe Reader.
Adobe Reader is free and available for download here:
