Mining Formal Concepts with a Bounded Number of Exceptions from Transactional Data

We are designing new data mining techniques on boolean contexts to identify a priori interesting bi-sets (i.e., sets of objects or transactions associated to sets of attributes or items). A typical important case concerns formal concept mining (i.e., maximal rectangles of true values or associated closed sets by means of the so-called Galois connection). It has been applied with some success to, e.g., gene expression data analysis where objects denote biological situations and attributes denote gene expression properties. However in such real-life application domains, it turns out that the Galois association is a too strong one when considering intrinsically noisy data. It is clear that strong associations that would however accept a bounded number of exceptions would be extremely useful. We study the new pattern domain of / concepts, i.e., consistent maximal bi-sets with less than false values per row and less than false values per column. We provide a complete algorithm that comput...

• Type: Conference
• Year: 2004
• Where: KDID
• Authors: Jérémy Besson, Céline Robardet, Jean-François Boulicaut
• Keywords: Database, False Values, Gene Expression, KDID 2004, Noisy Data