A new FCA algorithm enabling analyzing of complex and dynamic data sets

Abstract

Analyzing data with the use of Formal Concept Analysis (FCA) enables complex insights into hidden relationships between objects and features in a studied system. Several improvements in this research area, such as Fuzzy FCA or L-Fuzzy Concepts, bring the possibility to analyze data with a certain rate of indeterminacy. However, the usage of FCA on larger complex data brings several problems relating to the time-complexities of FCA algorithms and the size of generated concept lattices. The fuzzyfication of FCA emphasizes the mentioned problems. This article describes significant improvements of a selected FCA algorithm. The primary focus was given on the system of an effective data storage. The binary data was stored with the use of finite automata that leads to the lower memory consumption. Moreover, the better querying performance was achieved. Next, we focused on the inner process of the computation of all formal concepts. All improvements were integrated into a new FCA algorithm that can be used to analyze more complex data sets.