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

Santos, Phillip G.; Neves, Julio C. V.; Silva, Paula R. C.; Dias, Sérgio M.; Zárate, Luis Enrique; Song, Mark A. J.

doi:10.5220/0006775400500057

Cited by 4 publications

(8 citation statements)

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“…In [Santos et al, 2018], alterations to extract implications ProperIm in a dyadic formal context were proposed, applying BDDs in the structure to extract and manipulate proper rules. The ProperImplicBDD solution had a better execution runtime.…”

Section: Related Workmentioning

confidence: 99%

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Extracting concepts from triadic contexts using Binary Decision Diagram

Neves¹,

Zarate²,

Song³

2022

jucs

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Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

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Section: Related Workmentioning

confidence: 99%

“…In [Santos et al, 2018], the author's modifications proposed to extract proper implications [Ganter et al, 2005], ProperIm, from dyadic formal context, adding BDDs in the data structure. The ProperImplicBDD algorithm, proposed by the authors, presented better speedup.…”

Section: Introductionmentioning

confidence: 99%

Extracting concepts from triadic contexts using Binary Decision Diagram

Neves¹,

Zarate²,

Song³

2022

jucs

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“…Although the Impec and PropIm algorithms extract the proper implications, the computational performance of these algorithms processing high-dimensional contexts (in the number of instances) becomes impracticable. An algorithm called ProperImplicBDD, based on the PropIm [18], was proposed in [21]. The ProperImplicBDD algorithm uses a data structure known as Binary Decision Diagram (BDD) in order to manipulate high-dimensional formal contexts efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we introduce a new algorithm named ImplicPBDD (this being an evolution of the algorithm ProperImplicBDD [21]), which is capable of manipulating high-dimensional formal contexts.…”

Section: Introductionmentioning

confidence: 99%

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ImplicPBDD: A New Approach to Extract Proper Implications Set from High-Dimension Formal Contexts Using a Binary Decision Diagram †

et al. 2018

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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—up to 80% faster—than its original algorithm, regardless of the number of attributes, objects and densities.

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