Formal Concept Analysis for Knowledge Discovery
Formal concepts proved to be of big importance for knowledge discovery, both as a tool for concise representation of association rules and a tool for clustering and constructing taxonomies. The FCA4KD workshop aims at bringing together researchers working on diverse aspects of FCA-based knowledge extraction with the applications to fields like Computer and Information Science, Linguistics, Life and Social Sciences, Bioengineering, Chemistry, etc.
Topics of Interest
Main topics of interest include, but are not limited to:
- concept lattices and related structures
- attribute implications and data dependencies
- data preprocessing
- redundancy and dimensionality reduction
- information retrieval
- association rules and other data dependencies
Paper Submission and Publication
Papers may be submitted in PDF or Postscript format. Papers need to be formatted using the Springer Lecture Notes in Computer Science style.
The submission is to be done via EasyChair.
Knowledge structures and skill assignments: Structural tools for diagnostic assessment
The development of ontological model describing the behavior of mobile network subscribers
Query-based classification with interval pattern structures: application to credit scoring
Discovering Formal Contexts Generated from Conceptual Graphs
Notes on Relation Between Symbolic Classifiers
On Neural Network Architecture Based on Concept Lattices
Neural-Network Like Logical-Combinatorial Structure Of Data And The Possibilities Of Its Application For Constructing Concept Lattices