Abstract. It can be difficult to automatically generate “nice ” graphical representations for concept lattices from lexical databases, such as Roget’s Thesaurus, because the data sources tend to be large and complex. This paper discusses a variety of “data weeding ” techniques that can be applied in order to reduce the size of a concept lattice, first in general, and then with respect to Roget’s Thesaurus. The aim is that resulting lattices should display neither too much, nor too little information, independently of which search terms have been entered by a user. 1

We present an application of formal concept analysis aimed at creating and representing a meaningful structure of knowledge communities under the form of a lattice-based taxonomy built upon groups of agents jointly manipulating some notions. The resulting structure is however usually extremely complex, hence uneasy to comprehend. We consider two approaches to build a concise representation respecting the original taxonomy, while hiding uninteresting and superfluous information, using both a pruning strategy based on the notion of concept stability and a representational improvement based on nested line diagrams. We illustrate the method with a small community of embryologists. 1

Abstract. Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper, we use combination of Formal Concept Analysis and well-known matrix factorization methods to address computational complexity of social networks analysis and clarity of their visualization. The goal is to reduce the dimension of social network data and to measure the amount of information, which has been lost during the reduction. Presented example containing real data proves the feasibility of our approach. 1

Abstract—Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper we address computational complexity of social networks analysis and clarity of their visualization. Our approach uses combination of Formal Concept Analysis and well-known matrix factorization methods. The goal is to reduce the dimension of social network data and to measure the amount of information which is lost during the reduction. Keywords-concept lattice, two-mode social network, matrix factorization, correlation dimension I.

Abstract—Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper we address computational complexity of social networks analysis and clarity of their visualization. Our approach uses combination of Formal Concept Analysis and well-known matrix factorization methods. The goal is to reduce the dimension of social network data and to measure the amount of information which is lost during the reduction. Keywords-concept lattice, two-mode social network, matrix factorization, correlation dimension I.