FCA Analyst Session and Data Access Tools in FCART

Neznanov, Alexey; Parinov, Andrey

doi:10.1007/978-3-319-10554-3_21

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…For validating their methods, researchers create ad-hoc lists of duplicates using slightly transformed documents from standard collections. Now the situation is drastically better, see, for example, workshop series on Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection (PAN) 40 .…”

Section: Resultsmentioning

confidence: 99%

“…Originally, it was yet another FCA-based "integrated environment for knowledge and data engineers with a set of research tools based on Formal Concept Analysis" [39,40] featuring in addition work with unstructured data (including texts with various metadata) and Pattern Structures [41]. In its current distributed version, FCART consists of the following parts:…”

Section: Fca Tools and Practicementioning

confidence: 99%

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Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Ignatov

2015

Communications in Computer and Information Science

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Abstract. This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

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Section: Resultsmentioning

confidence: 99%

Section: Fca Tools and Practicementioning

confidence: 99%

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Ignatov

2015

Communications in Computer and Information Science

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“…Even though there are several good tools for building and managing concept lattices like Concept Explorer, we need to rely on more flexible tools with extensible components. In particular we hope that FCART can become our tool of choice in the near future [40].…”

Section: Discussionmentioning

confidence: 99%

Towards a Unified Taxonomy of Biclustering Methods

Ignatov¹,

Watson²

2017

Preprint

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Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.

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“…A routine was written to extract state/out-transition contexts from the AC-opt DFAs. These contexts were used as input data to FCART (Buzmakov & Neznanov, 2013;Neznanov & Parinov, 2014). The DHA variants under test generated FDFAs using as input the AC-opt DFAs as well as PAR derived from the resulting concept lattices.…”

Section: The Aho-corasick Experimentsmentioning

confidence: 99%

An Assessment of Algorithms for Deriving Failure Deterministic Finite Automata

Nxumalo

Kourie

Cleophas

et al. 2017

SACJ

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Failure deterministic finite automata (FDFAs) represent regular languages more compactly than deterministic finite automata (DFAs). Four algorithms that convert arbitrary DFAs to language-equivalent FDFAs are empirically investigated. Three are concrete variants of a previously published abstract algorithm, the DFA-Homomorphic Algorithm (DHA). The fourth builds a maximal spanning tree from the DFA to derive what it calls a delayed input DFA. A first suite of test data consists of DFAs that recognise randomised sets of finite length keywords. Since the classical Aho-Corasick algorithm builds an optimal FDFA from such a set (and only from such a set), it provides benchmark FDFAs against which the performance of the general algorithms can be compared. A second suite of test data consists of random DFAs generated by a specially designed algorithm that also builds language-equivalent FDFAs, some of which may have non-divergent cycles. These random FDFAs provide (not necessarily tight) lower bounds for assessing the effectiveness of the four general FDFA generating algorithms.

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FCA Analyst Session and Data Access Tools in FCART

Cited by 3 publications

References 9 publications

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields

Towards a Unified Taxonomy of Biclustering Methods

An Assessment of Algorithms for Deriving Failure Deterministic Finite Automata

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