SCGaz - A Synthetic Formal Context Generator with Density Control for Test and Evaluation of FCA Algorithms

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

doi:10.1109/smc.2013.591

Cited by 10 publications

(12 citation statements)

References 5 publications

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“…In both architecture types, we used 2 Intel ® Xeon ® processors E5‐2620 v2 @ 2.10GHz with 64GB RAM and 24 threads (6 SMT cores per package), and 2 Intel ® Xeon ® processors E5‐2699 v4 @ 2.20GHz with 64GB RAM and 88 threads (22 SMT cores per package). Finally, the input databases was created using the SCGaz, 31 a synthetic formal context generator.…”

Section: Methodsmentioning

confidence: 99%

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

Novais

Maciel

Souza

et al. 2021

Concurrency and Computation

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Algorithms for the extraction of formal concepts are widely studied in several areas of knowledge, such as finance, health, and statistics. However, these algorithms require high‐performance processing due to their combinatorial characteristics. In this work, an Open computing language (OpenCL)‐based Brute Force algorithm is proposed and evaluated for formal concept extraction on heterogeneous architectures (CPU+GPU and CPU+FPGA). The CPU+GPU architecture presents higher performance and scalability than other architectures when our Brute Force algorithm processes high dimensional contexts with many objects and attributes. Our parallel approach shows performance results up to 18× better than a smarter sequential algorithm called Data‐Peeler. Moreover, our Brute Force algorithm running on CPU+GPU architecture has greater energy efficiency, reaching at least 1.79× more operations per energy consumption than other algorithms on different architectures explored in this work.

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Section: Methodsmentioning

confidence: 99%

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

Novais

Maciel

Souza

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…At the same time, the tool allowing users to select any density in the bounds of the minimum and maximum permitted for a type of context. According to the authors [42], the tool allows a more controllable and reliable simulation environment.…”

Section: Methodsmentioning

confidence: 99%

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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“…However, this method is infeasible for practical use. Furthermore, the authors in [12] developed a synthetic formal context generator that employs a density measure. This generator is composed of multiple algorithms for different stages of the generation process, i.e., initialization to reach minimum density, regular filling and filling close to the maximum density.…”

Section: Related Workmentioning

confidence: 99%

Null Models for Formal Contexts

2020

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Null model generation for formal contexts is an important task in the realm of formal concept analysis. These random models are in particular useful for, but not limited to, comparing the performance of algorithms. Nonetheless, a thorough investigation of how to generate null models for formal contexts is absent. Thus we suggest a novel approach using Dirichlet distributions. We recollect and analyze the classical coin-toss model, recapitulate some of its shortcomings and examine its stochastic properties. Building upon this we propose a model which is capable of generating random formal contexts as well as null models for a given input context. Through an experimental evaluation we show that our approach is a significant improvement with respect to the variety of contexts generated. Furthermore, we demonstrate the applicability of our null models with respect to real world datasets.

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SCGaz - A Synthetic Formal Context Generator with Density Control for Test and Evaluation of FCA Algorithms

Cited by 10 publications

References 5 publications

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

An open computing language‐based parallel Brute Force algorithm for formal concept analysis on heterogeneous architectures

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

Null Models for Formal Contexts

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