Abstract
The efficiency and effectiveness of the retrieval of documents which are relevant to a certain topic or user query can be improved by means of the clustering of similar documents as well as by introducing parallel strategies. In this paper we explore the use of unsupervised learning, using clustering algorithms based on neural networks, as well as the introduction of NOW Architectures, a kind of low-cost parallel architecture, and study the impact on Information Retrieval.
The research reported in this paper has been supported in part under MEC and FEDER grant TIN2004-05920.
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Combarro, E.F., Ranilla, J., Mones, R., Vázquez, N., Díaz, I., Montañés, E. (2005). Unsupervised Learning in Information Retrieval Using NOW Architectures. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2005. EUROCAST 2005. Lecture Notes in Computer Science, vol 3643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556985_23
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DOI: https://doi.org/10.1007/11556985_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29002-5
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