Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
2002 (English)In: International Journal of Neural Systems (IJNS), ISSN 1793-6462, Vol. 12, no 6, 425-434 p.Article in journal (Refereed) Published
A novel algorithm for unsupervised fuzzy clustering is introduced. The algorithm uses a so-called Weighted Fixed Neural Network (WFNN) to store important and useful information about the topological relations in a given data set. The algorithm produces a weighted connected net, of weighted nodes connected by weighted edges, which reflects and preserves the topology of the input data set. The weights of the nodes and the edges in the resulting net are proportional to the local densities of data samples in input space. The connectedness of the net can be changed, and the higher the connectedness of the net is chosen, the fuzzier the system becomes. The new algorithm is computationally efficient when compared to other existing methods for clustering multi-dimensional data, such as color images.
Place, publisher, year, edition, pages
World Scientific Publishing Co. Pte Ltd, Singapore, 2002. Vol. 12, no 6, 425-434 p.
Unsupervised image segmentation; unsupervised fuzzy clustering; neuro-fuzzy systems; Weighted Fixed Neural Networks (WFNN); watersheds
Research subject Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-179672DOI: 10.1142/S0129065702001321OAI: oai:DiVA.org:kth-179672DiVA: diva2:885524
NR 201601212015-12-182015-12-182016-01-21Bibliographically approved