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- W2034014010 abstract "Clustering is a fundamental technique in data mining to identify essential group structures in a given data matrix. Traditional clustering methods are one-way clustering, which has however limitations for high-dimensional matrices or matrices with missing values. One possible solution is co-clustering, which does clustering both columns and rows simultaneously. Also auxiliary information over columns or rows is helpful to stabilize/improve the performance of clustering. We propose a new co-clustering approach, which can incorporate auxiliary information on both columns and rows. Our approach is based on a probabilistic model, for which we present an efficient method for estimating parameters, based on variational Bayesian learning. Our problem setting can be semi-supervised, by which our approach can be applied to various data mining applications. We evaluated the performance of the proposed approach by using both synthetic and real datasets, confirming the clear advantage of incorporating auxiliary information as well as of our method over two competing methods." @default.
- W2034014010 created "2016-06-24" @default.
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- W2034014010 date "2013-08-11" @default.
- W2034014010 modified "2023-09-24" @default.
- W2034014010 title "Variational Bayes co-clustering with auxiliary information" @default.
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- W2034014010 doi "https://doi.org/10.1145/2501006.2501012" @default.
- W2034014010 hasPublicationYear "2013" @default.
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