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Several algorithms that generate the set of all formal concepts and diagram graphs of concept lattices are considered. Some modifications of wellknown algorithms are proposed. Algorithmic complexity of the algorithms is studied both theoretically (in the worst case) and experimentally. Conditions of preferable use of some algorithms are given in terms of… (More)
An incremental concept lattice construction algorithm, called AddIntent, is proposed. In experimental comparison, AddIntent outperformed a selection of other published algorithms for most types of contexts and was close to the most efficient algorithm in other cases. The current best estimate for the algorithm’s upper bound complexity to construct a concept… (More)
We present an application of formal concept analysis aimed at representing a meaningful structure of knowledge communities in the form of a lattice-based taxonomy. The taxonomy groups together agents (community members) who develop a set of notions. If no constraints are imposed on how it is built, a knowledge community taxonomy may become extremely complex… (More)
Representing concept lattices constructed from large contexts often results in heavy, complex diagrams that can be impractical to handle and, eventually, to make sense of. In this respect, many concepts could allegedly be dropped from the lattice without impairing its relevance towards a taxonomy description task at a certain level of detail. We propose a… (More)
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… (More)
We propose a new algorithm constructing the canonical implication basis of a formal context. Being incremental, the algorithm processes a single attribute of the context at a single step. Experimental results bear witness to its competitiveness.
Implications of a formal context (G,M, I) have a minimal implication basis, called Duquenne-Guigues basis or stem base. It is shown that the problem of deciding whether a set of attributes is a premise of the stem base is in coNP and determining the size of the stem base is polynomially Turing equivalent to a #P-complete problem.