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Hebbian self-organizing integrate-and-fire networks for data clustering


Landis, F; Ott, T; Stoop, R (2010). Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Computation, 22(1):273-288.

Abstract

We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.

We propose a Hebbian learning-based data clustering algorithm using spiking neurons. The algorithm is capable of distinguishing between clusters and noisy background data and finds an arbitrary number of clusters of arbitrary shape. These properties render the approach particularly useful for visual scene segmentation into arbitrarily shaped homogeneous regions. We present several application examples, and in order to highlight the advantages and the weaknesses of our method, we systematically compare the results with those from standard methods such as the k-means and Ward's linkage clustering. The analysis demonstrates that not only the clustering ability of the proposed algorithm is more powerful than those of the two concurrent methods, the time complexity of the method is also more modest than that of its generally used strongest competitor.

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6 citations in Web of Science®
8 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2010
Deposited On:28 Feb 2010 10:00
Last Modified:05 Apr 2016 14:00
Publisher:MIT Press
ISSN:0899-7667
Additional Information:Copyright: MIT Press
Publisher DOI:10.1162/neco.2009.12-08-926
Related URLs:http://www.ini.uzh.ch/node/22238 (Organisation)
PubMed ID:19764879
Permanent URL: http://doi.org/10.5167/uzh-32022

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