Distributed Computation of Generalized One-Sided Concept Lattices on Sparse Data Tables

Authors

  • Peter Butka Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Košice
  • Jozef Pócs Department of Algebra and Geometry, Palacký University Olomouc, Olomouc, Czech Republic & Mathematical Institute, Slovak Academy of Sciences, Košice
  • Jana Pócsová BERG Faculty, Institute of Control and Informatization of Production Processes, Technical University of Košice, Košice

Keywords:

One-sided concept lattices, distributed algorithm, formal concept analysis, sparse data, text-mining

Abstract

In this paper we present the study on the usage of distributed version of the algorithm for generalized one-sided concept lattices (GOSCL), which provides a special case for fuzzy version of data analysis approach called formal concept analysis (FCA). The methods of this type create the conceptual model of the input data based on the theory of concept lattices and were successfully applied in several domains. GOSCL is able to create one-sided concept lattices for data tables with different attribute types processed as fuzzy sets. One of the problems with the creation of FCA-based models is their computational complexity. In order to reduce the computation times, we have designed the distributed version of the algorithm for GOSCL. The algorithm is able to work well especially for data where the number of newly generated concepts is reduced, i.e., for sparse input data tables which are often used in domains like text-mining and information retrieval. Therefore, we present the experimental results on sparse data tables in order to show the applicability of the algorithm on the generated data and the selected text-mining datasets.

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Published

2015-06-19

How to Cite

Butka, P., Pócs, J., & Pócsová, J. (2015). Distributed Computation of Generalized One-Sided Concept Lattices on Sparse Data Tables. Computing and Informatics, 34(1), 77–98. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/2034

Issue

Section

Special Section Articles