Authors:
Phillip Santos
1
;
Julio Neves
1
;
Paula Silva
1
;
Sérgio M. Dias
2
;
Luis Zárate
1
and
Mark Song
1
Affiliations:
1
Pontifical Catholic University of Minas Gerais (PUC Minas), Brazil
;
2
Federal Service of Data Processing (SERPRO), Brazil
Keyword(s):
Formal Concept Analysis, Proper Implications, Binary Decision Diagram.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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, r
egardless of the number of attributes, objetcts and densities.
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