Abstract
A biclustering algorithm, based on a greedy technique and enriched with a local search strategy to escape poor local minima, is proposed. The algorithm starts with an initial random solution and searches for a locally optimal solution by successive transformations that improve a gain function, combining the mean squared residue, the row variance, and the size of the bicluster. Different strategies to escape local minima are introduced and compared. Experimental results on yeast and lymphoma microarray data sets show that the method is able to find significant biclusters.
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Angiulli, F., Pizzuti, C. (2005). Gene Expression Biclustering Using Random Walk Strategies. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_50
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DOI: https://doi.org/10.1007/11546849_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
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