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Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
KTH, School of Technology and Health (STH), Health Systems Engineering.ORCID iD: 0000-0002-1831-9285
2002 (English)In: International Journal of Neural Systems (IJNS), ISSN 1793-6462, Vol. 12, no 6, 425-434 p.Article in journal (Refereed) Published
Abstract [en]

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.
Keyword [en]
Unsupervised image segmentation; unsupervised fuzzy clustering; neuro-fuzzy systems; Weighted Fixed Neural Networks (WFNN); watersheds
National Category
Signal Processing
Research subject
Computer Science
URN: urn:nbn:se:kth:diva-179672DOI: 10.1142/S0129065702001321OAI: diva2:885524

NR 20160121

Available from: 2015-12-18 Created: 2015-12-18 Last updated: 2016-01-21Bibliographically approved

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Hamid Muhammed, Hamed
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