Three Related FCA Methods for Mining Biclusters of Similar Values on Columns
Mehdi Kaytoue, Víctor Codocedo, Jaume Baixieres, Amedeo Napoli · 2014
Abstract. Biclustering numerical data tables consists in detecting par-ticular and strong associations between both subsets of objects and at-tributes. Such biclusters are interesting since they model the data as local patterns. Whereas there exists several definitions of biclusters, de-pending on the constraints they should respect, we focus in this paper on biclusters of similar values on columns. There are several ad hoc methods for mining such biclusters in the literature. We focus here on two aspects: genericity and efficiency. We show that Formal Concept Analysis pro-vides a mathematical framework to characterize them in several ways, but also to compute them with existing and efficient algorithms. The proposed methods, which rely on pattern structures and triadic concept analysis, are experimented and compared on two different datasets.