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Comparision Between Features of CbO based Algorithms for Generating Formal Concepts

机译:基于CbO的形式概念生成算法的特征之间的比较

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摘要

Formal Concept Analysis provides the mathematical notations for representing concepts and concept hierarchies making use of order and lattice theory. This has now been used in numerous applications which include software engineering, linguistics, sociology, information sciences, information technology, genetics, biology and in engineering. The algorithms derived from Kustenskov's CbO were found to provide the most efficient means of computing formal concepts in several research papers. In this paper key enhancements to the original CbO algorithms are discussed in detail. The effects of these key features are presented in both isolation and combination. Eight different variations of the CbO algorithms highlighting the key features were compared in a level playing field by presenting them using the same notation and implementing them from the notation in the same way. The three main enhancements considered are the partial closure with incremental closure of intents, inherited canonicity test failures and using a combined depth first and breadth first search. The algorithms were implemented in an un-optimized way to focus on the comparison on the algorithms themselves and not on any efficiencies provided by optimizing code. The main contribution of this paper is the complete comparison of the three main enhancements used in recent variations of the CbO based algorithms. The main findings were that there is a significant performance improvement partial closure with incremental closure of intents is used in isolation. However, there is no significant performance improvement when the depth and breadth first search or the inherited canonicity test failure feature is used in isolation. The inherited canonicity test failure needs to be combined with the combined depth and breadth first feature to obtain a performance increase. Combining all the three enhancements brought the best performance.
机译:形式概念分析使用顺序和格点理论为表示概念和概念层次结构提供了数学符号。现在,它已用于许多应用程序中,包括软件工程,语言学,社会学,信息科学,信息技术,遗传学,生物学和工程学。在几篇研究论文中,发现从库斯滕科夫的CbO派生的算法为计算形式概念提供了最有效的方法。本文详细讨论了对原始CbO算法的关键改进。这些关键功能的影响以隔离和组合的形式呈现。在一个公平竞争的环境中,比较了CbO算法的八个不同变体来突出关键功能,方法是使用相同的符号表示它们,并以相同的方式从符号中实现它们。所考虑的三个主要增强功能是部分关闭(使用意图的逐步关闭),继承的规范性测试失败以及使用组合的深度优先和广度优先搜索。这些算法以未优化的方式实现,以专注于算法本身的比较,而不关注优化代码所提供的任何效率。本文的主要贡献是对基于CbO的算法的最新变体中使用的三个主要增强功能的完整比较。主要发现是,局部隔离与意图隔离的渐进式关闭相比,部分关闭具有显着的性能改进。但是,如果单独使用深度和广度优先搜索或继承的规范性测试失败功能,则不会显着提高性能。需要将继承的规范测试失败与深度和广度优先特征相结合,以提高性能。将所有三个增强功能组合在一起可带来最佳性能。

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