Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Hebbian self-organizing integrate-and-fire networks for data clustering
Authors: Landis, Florian
Ott, Thomas
Stoop, Ruedi
DOI: 10.1162/neco.2009.12-08-926
Published in: Neural Computation
Volume(Issue): 22
Issue: 1
Page(s): 273
Pages to: 288
Issue Date: Jan-2010
Publisher / Ed. Institution: MIT Press
ISSN: 1530-888X
Language: English
Subjects: Clustering hebbian learning
Subject (DDC): 003: Systems
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.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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