Frequent item set mining is one of the best known and most popular data mining methods.
Originally developed for market basket analysis, it is used nowadays for almost any task that …

The task of finding correlations between items in a dataset, association mining, has received
considerable attention over the last decade. This article presents a survey of association …

Frequent itemsets play an essential role in many data mining tasks that try to find interesting
patterns from databases, such as association rules, correlations, sequences, episodes …

Recent studies on frequent itemset mining algorithms resulted in significant performance
improvements. However, if the minimal support threshold is set too low, or the data is highly …

This paper presents a new scalable algorithm for discovering closed frequent itemsets, a
lossless and condensed representation of all the frequent itemsets that can be mined from a …

Solving inductive queries which have to return complete collections of patterns satisfying a
given predicate has been studied extensively the last few years. The specific problem of …

All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning.
This principle allows for excluding candidate itemsets from the expensive counting phase. In …

Social bookmarking tools are rapidly emerging on the Web. In such systems users are
setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared …

We present a new method for the understandable description of local temporal relationships
in multivariate data, called Time Series Knowledge Mining (TSKM). We define the Time …

Association rules are a popular knowledge discovery technique for warehouse basket
analysis. They indicate which items of the warehouse are frequently bought together. The …