Recent interest in graph pattern mining has shifted from finding all frequent subgraphs to
obtaining a small subset of frequent subgraphs that are representative, discriminative or …

This paper tackles two challenges to discovery of graph rules. Existing discovery methods
often (a) return an excessive number of rules, and (b) do not scale with large graphs given …

We present an algorithm to extract an high-quality approximation of the (top-k) Frequent
itemsets (FIs) from random samples of a transactional dataset. With high probability the …

High utility itemset mining discovers itemsets whose utility is above a given threshold, where
the utility measures the importance of an itemset. It overcomes the limitation of frequent …

Rule discovery from large datasets is often prohibitively costly. The problem becomes more
staggering when the rules are collectively defined across multiple tables. To scale with large …

The tasks of extracting (top-K) Frequent Itemsets (FIs) and Association Rules (ARs) are
fundamental primitives in data mining and database applications. Exact algorithms for these …

“I'm an MC still as honest”–Eminem, Rap God We present MCRapper, an algorithm for
efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families …

We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality
at most w. To this purpose, we define an approximation to the top-K frequent itemsets to be a …

Many recommender systems use association rules mining, a technique that captures
relations between user interests and recommends new probable ones accordingly. Applying …

A thorough understanding of social media discussions and the demographics of the users
involved in these discussions has become critical for many applications like business or …