Abstract Association Rule Mining (ARM) is a significant task for discovering frequent patterns
in data mining. It has achieved great success in a plethora of applications such as market …

High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to
discovering all itemsets having a utility meeting a user-specified minimum utility threshold …

Previous studies have presented convincing arguments that a frequent pattern mining
algorithm should not mine all frequent patterns but only the closed ones because the latter …

Traditional association rule mining algorithms only generate a large number of highly
frequent rules, but these rules do not provide useful answers for what the high utility rules …

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as
well as for many other data mining tasks. Methods for mining frequent itemsets have been …

Mining frequent closed itemsets provides complete and non-redundant results for frequent
pattern analysis. Extensive studies have proposed various strategies for efficient frequent …

Efficient algorithms for mining frequent itemsets are crucial for mining association rules.
Methods for mining frequent itemsets and for iceberg data cube computation have been …

Association rule mining is one of the fundamental research topics in data mining and
knowledge discovery that identifies interesting relationships between itemsets in datasets …

Many applications track the movement of mobile objects, which can be represented as
sequences of timestamped locations. Given such a spatiotemporal series, we study the …

Discovering high utility itemsets in transaction databases is a key task for studying the
behavior of customers. It consists of finding groups of items bought together that yield a high …