Proceedings of: 11th International Conference on Concept Lattices and Their Applications (CLA 2014). Kosice, Slovakia, October 07-10, 2014.This paper explores the idea that a concept lattice is an information channel between objects and attributes. For this purpose we study the behaviour of incidences in L-formal contexts where L is the range of an information-theoretic entropy function. Examples of such data abound in machine learning and data mining, e.g. confusion matrices of multi-class classifers or document-term matrices. We use a wellmotivated information-theoretic heuristic, the maximization of mutual information, that in our conclusions provides a favour of feature selection providing and information-theory explanation of an established practice in Data Mining, Natural Language Processing and Information Retrieval applications, viz. stop-wording and frequency thresholding. We also introduce a post-clustering class identi cation in the presence of confusions and a favour of term selection for a multi-label document classifcation task.FJVA and AP are supported by EU FP7 project LiMoSINe (contract 288024) for this work. CPM has been supported by the Spanish Government-Comisión Interministerial de Ciencia y Tecnología project TEC2011-26807.Publicad
