In order to address the study of complex systems, the detection of patterns in their dynamics could play a key role in understanding their evolution. In particular, global patterns are required to detect emergent concepts and trends, some of them of a qualitative nature. Formal concept analysis (FCA) is a theory whose goal is to discover and extract knowledge from qualitative data (organized in concept lattices). In complex environments, such as sport competitions, the large amount of information currently available turns concept lattices into complex networks. The authors analyze how to apply FCA reasoning in order to increase confidence in sports predictions by means of detecting regularities from data through the management of intuitive and natural attributes extracted from publicly available information. The complexity of concept lattices -considered as networks with complex topological structure-is analyzed. It is applied to building a knowledge based system for confidence-based reasoning, which simulates how humans tend to avoid the complexity of concept networks by means of bounded reasoning skills.

Abstract. Formal Concept Analysis (FCA) is a theory whose goal is to discover and to extract Knowledge from qualitative data. It provides tools for reasoning with implication basis (and association rules). In this paper we analyse how to apply FCA reasoning to increase confidence in sports betting, by means of detecting temporal regularities from data. It is applied to build a Knowledge based system for confidence reasoning.

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Phenomenological reconstruction of a complex system (CS) from collected and selected data allows us to work with formal models (representations) of the system. The task of building a qualitative model necessitates the formalization of relationships among observations and concrete features. Formal concept analysis can help to understand the conceptual structure behind these qualitative representations by means of the so‐called concept lattices (CLs). The study of these kinds of semantic networks suggests that a strong relationship exists between its topological structure and its soundness/usefulness as a qualitative representation of the CS. The present paper is devoted to this question by presenting the so‐called scale‐free conceptualization hypothesis. The hypothesis claims that a scale‐free distribution of node connectivity appears on the CL associated to complex systems (CLCS) only when two requirements hold: CLCS is useful both to represent qualitative and reliable attributes on the CS, as well as to provide a basis for (qualitatively) successfully reasoning about the CS. Experiments revealed that the topologies of CLCS are similar when the amount of information on the CS is sufficient, whereas it is different in other CLs associated to random formal contexts or to other systems in which some of the former requirements do not hold. Copyright © 2013 John Wiley & Sons, Ltd.

A hybrid approach to phenomenological reconstruction of Complex Systems (CS), using Formal Concept Analysis (FCA) as main tool for conceptual data mining, is proposed. To illustrate the method, a classic CS is selected (cellular automata), to show how FCA can assist to predict CS evolution under different conceptual descriptions (from different observable features of the CS). Supported by TIC-6064 Excellence project (Junta de Andalucía) cofinanced with FEDER funds.