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Document Clustering using Learning from Examples
2012
International Journal of Computer Applications
Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user's interest with similar sets of terms representing the data items. Many techniques have been employed for constructing user profiles automatically, but they usually yield large sets of data. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user query. A new framework is described to classify large scale documents and retrieve the
doi:10.5120/4872-7299
fatcat:gsnr5anizrfy5ggulf3ugsrw7q