Evaluation of Different BDD Libraries to Extract Concepts in FCA – Perspectives and Limitations

Rimsa, Andrei; Zárate, Luis Enrique; Song, Mark A. J.

doi:10.1007/978-3-642-01970-8_36

Cited by 3 publications

(10 citation statements)

References 7 publications

(9 reference statements)

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“…The most relevant BDD libraries were evaluated in [26] and among them the CUDD-Colorado University Decision Diagram was chosen for providing function packages to work with Binary Decision Diagrams (BDDs) besides having more recent updates. The CUDD also has functions for Algebraic Decision Diagrams (ADDs) and Zero-suppressed Binary Decision Diagrams (ZDDs) that can be used to represent formal contexts.…”

Section: Binary Decision Diagrammentioning

confidence: 99%

“…In order to use BDDs to represent a the formal context, we employed a similar algorithm to the one proposed by [26].…”

Section: Representing a Formal Context With The Bddmentioning

confidence: 99%

“…In [26], the authors used BDD for extracting formal concepts, but their focus was on using different BDD libraries to check which library best could be used in FCA. [26] used brute force methods to obtain the set of intentions, and consequently the BDDs that represented the extensions. In all experiments performed in the study, the algorithms with BDD were more efficient when compared to the original algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…In all experiments performed in the study, the algorithms with BDD were more efficient when compared to the original algorithms. Similarly to the works of [26,[33][34][35], our study proposes to use BDD to process high-dimensional contexts, but BDD is used to represent and manipulate the formal context to extract implications.…”

Section: Related Workmentioning

confidence: 99%

“…A pre-processing step is performed to transform a formal context into its corresponding BDD. For the creation of the BDD representing the formal context, we used the generateBDDFormalContext algorithm, proposed by [26], as presented and described in Algorithm 1.…”

Section: The Proposed Algorithm Implicpbddmentioning

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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Section: Binary Decision Diagrammentioning

confidence: 99%

“…In order to use BDDs to represent a the formal context, we employed a similar algorithm to the one proposed by [26].…”

Section: Representing a Formal Context With The Bddmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: The Proposed Algorithm Implicpbddmentioning

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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Exploring Different Paradigms to Extract Proper Implications From High Dimensional Formal Contexts

et al. 2020

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Formal Concept Analysis (FCA) is an applied mathematical technique for data analysis, in which the relations between objects and attributes are identified. It introduces the notion of concepts and their hierarchical structure, from which we can obtain a set of implications between attributes that characterize a knowledge domain. The volume of information to be processed makes the use of FCA difficult in domains with a high number of dimensions, creating a demand for new solutions and algorithms for FCA applications. This article explores different approaches to extract proper implications from high dimensional contexts based on constraints to obtain the set of implications rules. We propose algorithms that use a data structure called Binary Decision Diagram (BDD) to represent the formal context, which reduces its size and, due to this, operates more efficiently. We also propose a heuristic to obtain proper implications by reducing the unnecessary generation of premises. In addition, we implemented a parallel computing model for generating and obtaining different implications. To analyze the proposed algorithms, we used different synthetic contexts with a varying number of objects, attributes, and density. The results obtained presented speed gains of up to 22 times when compared to the solutions proposed in the literature such as Impec and PropIm.

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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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Evaluation of Different BDD Libraries to Extract Concepts in FCA – Perspectives and Limitations

Cited by 3 publications

References 7 publications

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

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

Exploring Different Paradigms to Extract Proper Implications From High Dimensional Formal Contexts

Extracting concepts from triadic contexts using Binary Decision Diagram

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