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 …

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets,
yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper …

Abstract Weak isosinglet Neutral Heavy Leptons (vm) have been searched for using data
collected by the DELPHI detector corresponding to 3.3× 10 6 hadronic Z 0 decays at LEP1 …

With the advent of the Web along with the unprecedented amount of information available in
electronic format, conceptual data analysis is more useful and practical than ever, because …

The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets,
yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper …

We introduce the notion of iceberg concept lattices and show their use in knowledge
discovery in databases. Iceberg lattices are a conceptual clustering method, which is well …

The traditional association rule mining framework produces many redundant rules. The
extent of redundancy is a lot larger than previously suspected. We present a new framework …

The class of closed patterns is a well known condensed representations of frequent patterns,
and have recently attracted considerable interest. In this paper, we propose an efficient …