Our goals are to develop the theory of FCA, especially for higher-adic data sets as well as to develop efficient algorithms for mining and representing knowledge structures.

FCA uses an intuitive yet powerful graphical representation for knowledge structures as well as efficient algorithms to mine for knowledge patterns encoded in various data sets.


FCA Tools Bundle

FCA Tools Bundle is a website containing a collection of tools that can be used for conceptual structure analysis.

Research Seminars - Academic year 2017-2018

Usually the meetings are held in 406 room, 58-60 Teodor Mihali street, each Monday at 14:00.


Navigation and Exploration Tool for Polyadic FCA

Abstract: Formal concept analysis (FCA) is a powerful mathematical tool that allows deriving concept hierarchies from large sets of data in order to analyze data and derive meaningful information from it [3]. FCA finds practical application in fields including data mining, text mining, machine learning, knowledge management, semantic web, software development, chemistry, biology and many more. FCA works with contexts and concept lattices derived from them. Since navigation in three-dimensional spaces is rather difficult, one method of navigation in tricontexts uses local projections and reduces the triadic contexts to multiple dyadic contexts. The purpose of this paper is to present FCA Tools Bundle, which is a collection of tools for dyadic and triadic formal concept analysis. Furthermore, in this paper, we describe the architecture of the tool and the technologies used in its implementation.

Keywords: FCA, Triadic Context, Navigation Tool, Concept Lattice

FACT – A Tool for Temporal Formal Concept Analysis

Abstract: Formal Concept Analysis offers an elegant, intuitive and powerful graphical representation of landscapes of knowledge as concept lattices. In this paper, we report about the current state of FACT, a tool for temporal data analysis and knowledge discovery. FACT is a web-based application which allows an online interaction with larger data sets in order to explore and analyze data conceptually. It uses concept lattices in order to extract knowledge from the data set. After presenting the tool itself we shortly describe an example and present the planning for further development.

Keywords: Life tracks, Temporal Concept Analysis, Web logs analysis, Conceptual structures, User behavior, Attractors

An investigation of user behavior in educational platforms using Temporal Concept Analysis

Abstract: In this paper, we focus on the problem of investigating user behavior using conceptual structures distilled from weblogs of an educational e-platform. We define a set of so-called attractors as sets of scales in conceptual time systems and compute user life tracks in order to highlight different types of behaviors. These life tracks can give valuable feedback to the instructor how his students are using the online educational resources, analyzing their behavior and extracting as much knowledge as possible from the log access files. This might also be helpful to analyze the usability of the online educational content, eventually for improving the structure of the platform and to develop new educational instruments.

Keywords: Life tracks, Temporal Concept Analysis, Web logs analysis, Conceptual structures, User behavior, Attractors