2015•International Journal of Autonomous and Adaptive Communications SystemsRequires access

Formal concept analysis and concept lattice: perspectives and challenges

Hehua Yan, Caifeng Zou, Jianqi Liu, Zhonghai Wang

Open publisher page 7 citations

Abstract

Formal concept analysis (FCA) is a powerful tool for data mining, ontology research, web semantic retrieval, software engineering, and knowledge discovery. Concept lattice is the core data structure of FCA. Association rules mining methods based on concept lattices are discussed. The algorithms of constructing concept lattices are introduced, and the merits and drawbacks of these algorithms are compared. The research situation about attribute reduction of concept lattice is given. Furthermore, the extended models of concept lattice and the challenges to development of concept lattice are introduced. At last, many problems on FCA and concept lattice needed to study deeply are given.

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What this paper is about

Formal concept analysis (FCA) is a powerful tool for data mining, ontology research, web semantic retrieval, software engineering, and knowledge discovery. Concept lattice is the core data structure of FCA. Association rules mining methods based on concept lattices are discussed. The algorithms of constructing concept lattices are introduced, and the merits and drawbacks of these algorithms are compared. The research situation about attribute reduction of concept lattice is given. Furthermore, the extended models of concept lattice and the challenges to development of concept lattice are introduced. At last, many problems on FCA and concept lattice needed to study deeply are given.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Formal concept analysis (FCA) is a powerful tool for data mining, ontology research, web semantic retrieval, software engineering, and knowledge discovery. Concept lattice is the core data structure of FCA. Association rules mining methods based on concept lattices are discussed. The algorithms of constructing concept lattices are introduced, and the merits and drawbacks of these algorithms are compared. The research situation about attribute reduction of concept lattice is given. Furthermore, the extended models of concept lattice and the challenges to development of concept lattice are introduced. At last, many problems on FCA and concept lattice needed to study deeply are given.

Key concepts: Lattice Miner, Formal concept analysis, Computer science, Lattice (music), Association rule learning, Data mining, Theoretical computer science, Information retrieval

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