Cluster assessment remains one of the most actual problems in data mining. In this paper, a new approach to the selection of clusters based on a combination of measures of cluster quality is proposed. The new approach incorporates easily expert understanding of “interestingness” of clusters and does not require pre-defined parameters and thresholds. The subset of selected clusters is small and can be analyzed easily by experts.
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.