The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Self organization of a massive document collection
Abstract
This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the self-organizing map (SOM) algorithm. As the feature vectors for the documents statistical representations of their vocabularies are used. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.
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Citations
Cites
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
Self-citation
Cited by
LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections
Ampazis, N., & Perantonis, S. J. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000023449.95030.8f · Cited by 18
High-performance dynamic quantum clustering on graphics processors
Wittek, P. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.048 · Cited by 16
Self-Tuned Descriptive Document Clustering Using a Predictive Network
Brockmeier, A. J., Mu, T., Ananiadou, S., & Goulermas, J. Y. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2781721 · Cited by 15
Metadata
- Authors
- Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., & Saarela, A.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000-05
- Volume
- 11
- Issue
- 3
- Pages
- 574-585
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2162161511
- Cited by
- 908
- References
- 1
- FWCI
- 69.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., & Saarela, A. (2000). Self organization of a massive document collection. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.846729
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
