Abstract. We present an information-theoretic framework for mining dependencies between itemsets in binary data. The problem of closure-based redundancy in this context is theoreti...
The problem of selecting small groups of itemsets that represent the data well has recently gained a lot of attention. We approach the problem by searching for the itemsets that c...
Frequent behavioural pattern mining is a very important topic of knowledge discovery, intended to extract correlations between items recorded in large databases or Web acces logs....
Abstract In recent years, researchers have begun to study inductive databases, a new generation of databases for leveraging decision support applications. In this context, the user...
Subspace clustering and frequent itemset mining via “stepby-step” algorithms that search the subspace/pattern lattice in a top-down or bottom-up fashion do not scale to large ...