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dc.contributor.authorLandis, Florian-
dc.contributor.authorOtt, Thomas-
dc.contributor.authorStoop, Ruedi-
dc.date.accessioned2018-03-28T14:30:26Z-
dc.date.available2018-03-28T14:30:26Z-
dc.date.issued2010-01-
dc.identifier.issn1530-888Xde_CH
dc.identifier.issn0899-7667de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/4403-
dc.description.abstractWe 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.de_CH
dc.language.isoende_CH
dc.publisherMIT Pressde_CH
dc.relation.ispartofNeural Computationde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectClustering hebbian learningde_CH
dc.subject.ddc003: Systemede_CH
dc.titleHebbian self-organizing integrate-and-fire networks for data clusteringde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1162/neco.2009.12-08-926de_CH
dc.identifier.pmid19764879de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end288de_CH
zhaw.pages.start273de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume22de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
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Landis, F., Ott, T., & Stoop, R. (2010). Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Computation, 22(1), 273–288. https://doi.org/10.1162/neco.2009.12-08-926
Landis, F., Ott, T. and Stoop, R. (2010) ‘Hebbian self-organizing integrate-and-fire networks for data clustering’, Neural Computation, 22(1), pp. 273–288. Available at: https://doi.org/10.1162/neco.2009.12-08-926.
F. Landis, T. Ott, and R. Stoop, “Hebbian self-organizing integrate-and-fire networks for data clustering,” Neural Computation, vol. 22, no. 1, pp. 273–288, Jan. 2010, doi: 10.1162/neco.2009.12-08-926.
LANDIS, Florian, Thomas OTT und Ruedi STOOP, 2010. Hebbian self-organizing integrate-and-fire networks for data clustering. Neural Computation. Januar 2010. Bd. 22, Nr. 1, S. 273–288. DOI 10.1162/neco.2009.12-08-926
Landis, Florian, Thomas Ott, and Ruedi Stoop. 2010. “Hebbian Self-Organizing Integrate-and-Fire Networks for Data Clustering.” Neural Computation 22 (1): 273–88. https://doi.org/10.1162/neco.2009.12-08-926.
Landis, Florian, et al. “Hebbian Self-Organizing Integrate-and-Fire Networks for Data Clustering.” Neural Computation, vol. 22, no. 1, Jan. 2010, pp. 273–88, https://doi.org/10.1162/neco.2009.12-08-926.


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