A study of the use of self‐organising maps in information retrieval
Jyri Saarikoski, Jorma P. S. Laurikkala, Kalervo Järvelin, Martti Juhola · Journal of Documentation · 2009
Purpose -We studied the applicability of self-organising maps for searching for information in a document collection.Design / methodology / approach -After conventional preprocessing, like transform into vector space, documents from a German document collection were trained for a neural network of Kohonen selforganising map type.Such an unsupervised network forms a document map from which relevant objects can be found according to queries.Findings -Selforganising maps ordered documents to groups from which it was possible to find relevant targets.Research limitations / implications -The number of documents used was moderate due to the limited number of documents associated to test topics.The training of self-organising maps entails rather long running times, which is their practical limitation.In future, our aim will be to build larger networks by This research was funded, in part, by the Academy of Finland, Project Nos.120996, 200844, 202185, 204970 and 206568.The SNOWBALL stemmer by Martin Porter.Moya-Anegón, F., Herrero-Solana, V. and Jiménez-Contreras, E. (2006), " A connectionist and multivariate approach to science maps: the SOM, clustering and MDS applied to library and information science research" , Journal of Information Science,