| 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 0899-7667 |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/4403 |
| 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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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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