An Algorithm to Find Frequent Concepts of a Formal Context with Taxonomy

Formal Concept Analysis (FCA) considers attributes as a non-ordered set. This is appropriate when the data set is not structured. When an attribute taxonomy exists, existing techniques produce a completed context with all attributes deduced from the taxonomy. Usual algorithms can then be applied on the completed context for finding frequent concepts, but the results systematically contain redundant information. This article describes an algorithm which allows the frequent concepts of a formal context with taxonomy to be computed. It works on a non-completed context and uses the taxonomy information when needed. The results avoid the redundancy problem with equivalent performance.

• Type: Conference
• Year: 2006
• Where: CLA
• Authors: Peggy Cellier, Sébastien Ferré, Olivier Ridoux, Mireille Ducassé
• Keywords: Attribute Taxonomy, CLA 2006, CLA 2007, Formal Concept Analysis, Frequent Concepts