ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †
Autor: | Sérgio M. Dias, Mark A. J. Song, Phillip G. Santos, Julio C. V. Neves, Luis Enrique Zárate, Pedro H. B. Ruas, Paula R. C. Silva |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Thesaurus (information retrieval)
Theoretical computer science lcsh:T58.5-58.64 Binary decision diagram Computer science lcsh:Information technology Context (language use) 02 engineering and technology Data structure binary decision diagram Set (abstract data type) formal concept analysis 020204 information systems 0202 electrical engineering electronic engineering information engineering Formal concept analysis 020201 artificial intelligence & image processing Dimension (data warehouse) Representation (mathematics) Information Systems proper implication |
Zdroj: | Information, Vol 9, Iss 11, p 266 (2018) Information Volume 9 Issue 11 |
ISSN: | 2078-2489 |
Popis: | Formal concept analysis (FCA) is largely applied in different areas. However, in some FCA applications the volume of information that needs to be processed can become unfeasible. Thus, the demand for new approaches and algorithms that enable processing large amounts of information is increasing substantially. This article presents a new algorithm for extracting proper implications from high-dimensional contexts. The proposed algorithm, called ImplicPBDD, was based on the PropIm algorithm, and uses a data structure called binary decision diagram (BDD) to simplify the representation of the formal context and enhance the extraction of proper implications. In order to analyze the performance of the ImplicPBDD algorithm, we performed tests using synthetic contexts varying the number of objects, attributes and context density. The experiments show that ImplicPBDD has a better performance&mdash up to 80% faster&mdash than its original algorithm, regardless of the number of attributes, objects and densities. |
Databáze: | OpenAIRE |
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