An Improved Heuristic for Consistent Biclustering Problems
نویسندگان
چکیده
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 S1, S2, . . . , Sk and F1, F2, . . . , Fk denote the classes of the samples (columns) and features (rows), respectively. Formally biclustering can be defined as follows.
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