Self organization of a massive document collection

by: T Kohonen, S Kaski, K Lagus, J Salojarvi, J Honkela, V Paatero, A Saarela

Neural Networks, IEEE Transactions on, Vol. 11, No. 3. (2000), pp. 574-585.

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Abstract

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 6840568 patent abstracts onto a 1002240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms

@article{citeulike:588715, abstract = {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 6840568 patent abstracts onto a 1002240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms}, author = {Kohonen, T. and Kaski, S. and Lagus, K. and Salojarvi, J. and Honkela, J. and Paatero, V. and Saarela, A. }, citeulike-article-id = {588715}, doi = {10.1109/72.846729}, journal = {Neural Networks, IEEE Transactions on}, keywords = {kohonengroup, som, textmining}, number = {3}, pages = {574--585}, posted-at = {2008-07-06 01:06:45}, priority = {5}, title = {Self organization of a massive document collection}, url = {http://dx.doi.org/10.1109/72.846729}, volume = {11}, year = {2000} }

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TY - JOUR ID - citeulike:588715 L3 - citeulike-article-id:588715 TI - Self organization of a massive document collection JF - Neural Networks, IEEE Transactions on VL - 11 IS - 3 SP - 574 EP - 585 N2 - 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 6840568 patent abstracts onto a 1002240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms KW - kohonengroup KW - som KW - textmining AU - Kohonen, T AU - Kaski, S AU - Lagus, K AU - Salojarvi, J AU - Honkela, J AU - Paatero, V AU - Saarela, A PY - 2000/// UR - http://dx.doi.org/10.1109/72.846729 ER -

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