A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be c...
We propose a method for induction of compact optimal recommendation policies based on discovery of frequent itemsets in a purchase database, followed by the application of standar...
In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard probl...
Frequent-pattern mining has been studied extensively on scalable methods for mining various kinds of patterns including itemsets, sequences, and graphs. However, the bottleneck of...
High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...