Learning of Simple Conceptual Graphs from Positive and Negative Examples
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Two FCA-Based Methods for Mining Gene Expression Data
ICFCA '09 Proceedings of the 7th International Conference on Formal Concept Analysis
FcaBedrock, a formal context creator
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Towards fault-tolerant formal concept analysis
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Formal concept discovery in semantic web data
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
A conceptual approach to gene expression analysis enhanced by visual analytics
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Discovering Knowledge in Data Using Formal Concept Analysis
International Journal of Distributed Systems and Technologies
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This paper presents a program, called In-Close2, that is a high performance realisation of the Close-by-One (CbO) algorithm. The design of In-Close2 is discussed and some new optimisation and data preprocessing techniques are presented. The performance of In-Close2 is favourably compared with another contemporary CbO variant called FCbO. An application of In-Close2 is given, using minimum support to reduce the size and complexity of a large formal context. Based on this application, an analysis of gene expression data is presented. In-Close2 can be downloaded from Sourceforge.