International Conference on Formal Concept Analysis, Rennes, Frankreich

Abbrevation

ICFCA

Link

http://icfca2017.irisa.fr/

City

Rennes

Country

France

Deadline Paper

2016-12-19

Start Date

2017-06-12

End Date

2017-06-16

Abstract

Formal Concept Analysis emerged in the 1980′s from attempts to restructure lattice theory in order to promote better communication between lattice theorists and potential users of lattice theory. Since its early years, Formal Concept Analysis has developed into a research field in its own right with a thriving theoretical community and a rapidly expanding range of applications in information and knowledge processing including visualization, data analysis (mining) and knowledge management and discovery. The ICFCA conference series aims at bringing together researchers and practitioners working on theoretical or applied aspects of Formal Concept Analysis within major related areas such as Mathematics and Computer and Information Sciences and their diverse applications to fields like Software Engineering, Linguistics, Life and Social Sciences, etc. All accepted submissions will be refereed, and the conference proceedings will be published by Springer in the LNCS/LNAI series. * Topics Main topics of interest include, but are not limited to: ** Fundamental aspects of FCA – FCA theory – Lattice theory – Lattice drawing – Philosophical foundations – Algorithms and complexity theory ** Bridging FCA to information sciences and artificial intelligence – FCA and logic, e.g., semantic web, description logics – FCA and conceptual structures, e.g., concept graphs, knowledge spaces – FCA and data analysis, e.g., hierarchical classification, data organization – FCA and data mining, e.g., pattern mining – FCA and information retrieval, e.g. exploratory search, navigation, querying – FCA and machine learning, e.g., learning with hypothesis, feature selection – FCA and database theory, e.g., dependencies, rules, data cubes – FCA and software engineering – FCA and morphological mathematics ** Understanding, modelling real–world data and phenomena with FCA – Analysis of "big data" with FCA, e.g., sampling, parallel computing – Analysis of social networks and their dynamics – Applications for scientific data analysis, e.g., in biology or health care – Applications for sensors data and user interactions traces – Other FCA applications