An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram

Phillip Santos, Julio Neves, Paula Silva, Sérgio M. Dias, Luis Zárate, Mark Song

2018

Abstract

Formal concept analysis (FCA) is currently used in a large number of applications in different areas. However, in some applications the volume of information that needs to be processed may become infeasible. Thus, demand for new approaches and algorithms to enable the processing of large amounts of information is increasing substantially. This paper presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, ProperImplicBDD, was based on the PropIm algorithm. Using a data structure called binary decision diagram (BDD) it is possible to simplify the representation of the formal context and to improve the performance on extracting proper implications. In order to analyze the performance of the ProperImplicBDD algorithm, we performed tests using synthetic contexts varying the number of attributes and context density. The experiments shown that ProperImplicBDD has a better perfomance – up to 8 times faster – than the original one, regardless of the number of attributes, objetcts and densities.

Paper Citation

in Harvard Style

Santos P., Neves J., Silva P., Dias S., Zárate L. and Song M. (2018). An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-298-1, pages 50-57. DOI: 10.5220/0006775400500057

in Bibtex Style

@conference{iceis18,
author={Phillip Santos and Julio Neves and Paula Silva and Sérgio M. Dias and Luis Zárate and Mark Song},
title={An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2018},
pages={50-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006775400500057},
isbn={978-989-758-298-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Approach to Extract Proper Implications Set from High-dimension Formal Contexts using Binary Decision Diagram
SN - 978-989-758-298-1
AU - Santos P.
AU - Neves J.
AU - Silva P.
AU - Dias S.
AU - Zárate L.
AU - Song M.
PY - 2018
SP - 50
EP - 57
DO - 10.5220/0006775400500057