Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices

Antoni, L’ubomír; Eliaš, Peter; Horváth, Tomáš; Krajči, Stanislav; Krídlo, Ondrej; Török, Csaba

Abstract

Formal Concept Analysis identifies hidden patterns in data that can be presented to the user or the data analyst. We propose a method for analyzing the correlation matrices based on Formal concept analysis. In particular, we define a notion of squared symmetric formal context and prove its properties. Transforming a correlation matrix into a squared symmetric formal context is feasible with the help of fuzzy logic. Thus, the concept hierarchies of squared symmetric formal contexts can be thoroughly investigated and visualized. Moreover, information hidden in such type of data can help to find some interrelations between attributes and can help to solve pending issues within enterprise or science. To illustrate our approach, we include our novel results by analyzing a correlation matrix with 36 variables computed from a real dataset.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: International Conference on Conceptual Structures (ICCS) 2023
• Start Date: Sep 11, 2023
• End Date: Sep 13, 2023
• Online Publication Date: Aug 16, 2023
• Publication Date: 2023
• Deposit Date: Apr 8, 2024
• Publisher: Springer
• Pages: 19-27
• Series Title: Lecture Notes in Artificial Intelligence
• Series Number: 14133
• Series ISSN: 0302-9743
• Book Title: Graph-Based Representation and Reasoning: 28th International Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September 11–13, 2023, Proceedings
• ISBN: 9783031409592
• DOI: https://doi.org/10.1007/978-3-031-40960-8_2
• Public URL: http://researchrepository.napier.ac.uk/Output/3587429