Formal concept analysis and concept lattice: perspectives and challenges
Hehua Yan, Caifeng Zou, Jianqi Liu, Zhonghai Wang
Abstract
Hehua Yan, Caifeng Zou, Jianqi Liu, Zhonghai Wang
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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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