On interestingness measures of formal concepts
  Formal concepts and closed itemsets proved to be of big importance for
knowledge discovery, both as a tool for concise representation of association
rules and a tool for clustering and constructing domain taxonomies and
ontologies. Exponential explosion makes it difficult to consider the whole
concept lattice arising from data, one needs to select most useful and
interesting concepts. In this paper interestingness measures of concepts are
considered and compared with respect to various aspects, such as efficiency of
computation and applicability to noisy data and performing ranking correlation.
