Abstract
Let matrix A represent a data set of m features and n samples. Each element of the matrix, aij, corresponds to the expression of the i-th feature in the j-th sample. Biclustering is a classification of the samples as well as features into k classes. In other words, we need to classify columns and rows of the matrix A. Doing so, let S 1, S 2,..., S k and F 1, F 2,..., F k denote the classes of the samples (columns) and features (rows), respectively. Formally biclustering can be defined as follows.
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Nahapetyan, A., Busygin, S., Pardalos, P. (2008). The Improved Heuristic for Consistent Biclustering Problems. In: Mondaini, R.P., Pardalos, P.M. (eds) Mathematical Modelling of Biosystems. Applied Optimization, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76784-8_5
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DOI: https://doi.org/10.1007/978-3-540-76784-8_5
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