Comparing and analyzing the computational complexity of FCA algorithms

Strok, Fedor; Neznanov, Alexey

doi:10.1145/1899503.1899557

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…Here a series of carefully controlled experiments were designed and conducted. Andrews, 2015;Krajca, Outrata, & Vychodil, 2010;Kuznetsov & Obiedkov, 2002;Outrata, 2015;Strok & Neznanov, 2010 have all used a similar approach to compare actual programs that implement a set of algorithms.…”

Section: Methodology 21 Introductionmentioning

confidence: 99%

“…The parallel algorithms presented in this thesis are parallel variants of the fastest serial FCA algorithm. Several researches have concluded through empirical testing that the CbO family of algorithms provides the best performance (Andrews, 2009, Krajca et al, 2008, 2010aOutrata & Vychodil, 2012;Strok & Neznanov, 2010). The first sub question on identifying the best serial algorithm is presented in detail in Chapter 4.…”

Section: Objectives and Structure Of The Thesismentioning

confidence: 99%

“…Although there are no standard dataset established for testing FCA algorithms several research studies have used publicly available datasets for this purpose (Andrews, 2009(Andrews, , 2011(Andrews, , 2015Frank & Asuncion, 2010;Krajca, Outrata, & Vychodil, 2008;Krajca et al, 2010;Strok & Neznanov, 2010). In this research several popular public datasets such as the mushroom dataset, adult dataset from the UCI learning repository were used.…”

Section: Benchmarking Of the Implementationsmentioning

confidence: 99%

“…Computing formal concepts is a fundamental study in its own right and there are a number of algorithms that focus on this without computing the lattice (Outrata & Vychodil, 2012;Strok & Neznanov, 2010).…”

Section: Fca Algorithmsmentioning

confidence: 99%

“…Algorithms such as NextClosure, Close by One (CbO) makes use of a lexicographical order in the generation of concepts and thus avoids searching through the generated concepts (Berry et al, 2007). Out of the many algorithms that have been developed (Andrews, 2009, Krajca, Outrata, & Vychodil, 2008Outrata & Vychodil, 2012;Strok & Neznanov, 2010), have noted that the CbO based algorithms produce the best results for different types of datasets.…”

Section: Limitations Of Serial Fca Algorithmsmentioning

confidence: 99%

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Parallelization of formal concept analysis algorithms

Kodagoda¹

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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 thesis 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. Publications The following research articles were published based on the research work carried out.

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Section: Methodology 21 Introductionmentioning

confidence: 99%

Section: Objectives and Structure Of The Thesismentioning

confidence: 99%

Section: Benchmarking Of the Implementationsmentioning

confidence: 99%

Section: Fca Algorithmsmentioning

confidence: 99%

Section: Limitations Of Serial Fca Algorithmsmentioning

confidence: 99%

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Parallelization of formal concept analysis algorithms

Kodagoda¹

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A Partial-Closure Canonicity Test to Increase the Efficiency of CbO-Type Algorithms

Andrews

2014

Graph-Based Representation and Reasoning

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Abstract. Computing formal concepts is a fundamental part of Formal Concept Analysis and the design of increasingly efficient algorithms to carry out this task is a continuing strand of FCA research. Most approaches suffer from the repeated computation of the same formal concepts and, initially, algorithms concentrated on efficient searches through already computed results to detect these repeats, until the so-called canonicity test was introduced. The canonicity test meant that it was sufficient to examine the attributes of a computed concept to determine its newness: searching through previously computed concepts was no longer necessary. The employment of this test in Close-by-One type algorithms has proved to be highly effective. The typical CbO approach is to compute (close) a concept and then test its canonicity. This paper describes a more efficient approach, whereby a concept need only be partially closed in order to carry out the test. Only if it passes the test does its closure need to be completed. This paper presents this partialclosure canonicity test in the In-Close algorithm and compares this to a traditional CbO algorithm to demonstrate the increase in efficiency.

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