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Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2004 (English)In: International Journal of Neural Systems (IJNS), Vol. 14, no 6, 355-371 p.Article in journal (Refereed) Published
Abstract [en]

A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information bout the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of the resulting clusters is determined by this procedure. Only two parameters must be chosen by the user for the FC-WINN algorithm to determine the resolution and the connectedness of the net. Other parameters that must be specified are those which are necessary for the used incremental neural network, which is a modified version of the Growing Neural Gas algorithm (GNG). The FC-WINN algorithm is computationally efficient when compared to other approaches for clustering large high-dimensional data sets.

Place, publisher, year, edition, pages
2004. Vol. 14, no 6, 355-371 p.
Keyword [en]
Unsupervised fuzzy clustering, unsupervised image segmentation, neuro-fuzzy systems, Growing Neural Gas (GNG), incremental neural networks, watersheds, data reduction
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-67751OAI: oai:DiVA.org:uu-67751DiVA: diva2:95662
Available from: 2005-05-23 Created: 2005-05-23 Last updated: 2011-01-12

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