Abstract
Abstract The data mining task of finding coherent-signed bicluster is not new in the field of gene expression data. It could also be applied in the area of computation-oriented biodiversity study with a significant impact for exposing domain-specific coherency. The present study considers a symbolic table filled with signs having meaning imposed by the users and proposes a novel signed biclustering methodology using formal concept analysis. The present work has the ability to identify both the constant and coherent-signed biclusters. Moreover, aiming at revealing the usefulness of the proposed approach, we prepare a signed data set corresponding to the spatio-temporal changes of abundance data of Sundarban mangroves, the vulnerable mangrove ecosystem. In this article, we explain our methodology theoretically with the help of a related but smaller synthetic data set.KeywordsBiclusterBiodiversityEcosystemData miningConstant and coherent clustersFormal concept analysis
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