Abstract
Concept lattices built on noisy data tend to be large and hence hard to interpret. We introduce several measures that can be used in selecting relevant concepts and discuss how they can be combined together. We study their performance in a series of experiments. © Springer-Verlag Berlin Heidelberg 2010.
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CITATION STYLE
APA
Klimushkin, M., Obiedkov, S., & Roth, C. (2010). Approaches to the selection of relevant concepts in the case of noisy data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5986 LNAI, pp. 255–266). https://doi.org/10.1007/978-3-642-11928-6_18
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