Knowledge acquisition by methods of formal concept analysis
Proceedings of the conference on Data analysis, learning symbolic and numeric knowledge
Incremental clustering for dynamic information processing
ACM Transactions on Information Systems (TOIS)
International Journal of Man-Machine Studies
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Automatic Structuring of Knowledge Bases by Conceptual Clustering
IEEE Transactions on Knowledge and Data Engineering
Boosting Formal Concepts to Discover Classification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Adaptive learning of nominal concepts for supervised classification
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A bottom-up algorithm of vertical assembling concept lattices
International Journal of Data Mining and Bioinformatics
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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In many real-world learning problems the data flows continuously and learning algorithms should be able to respond to this circumstance: the induced concept description should gradually change over time. In this paper, we outline some existing incremental learners based on the theory of Formal Concept Analysis: FCA. Then, we introduce a new learning approach that improves incremental concept formation. This approach has the advantage of handling both the problem of data addition, data deletion, data update, attribute addition and attribute deletion. Finally, we apply the proposed approach to the problem of cancer diagnosis. We measure the effect of incrementality on the quality of the discovered rules using cross-validation.