Interestingness measures play an important role in data mining, regardless of the kind of
patterns being mined. These measures are intended for selecting and ranking patterns …

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 …

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 …

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying
interesting subgroups according to some property of interest. This article summarizes …

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 …

Analyzing bank databases for customer behavior management is difficult since bank
databases are multi-dimensional, comprised of monthly account records and daily …

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 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 …

Recently, by combining rough set theory with granular computing, pessimistic and optimistic
multigranulation rough sets have been proposed to derive “AND” and “OR” decision rules …

In this paper, we propose the algorithm PASCAL which introduces a novel optimization of
the well-known algorithm Apriori. This optimization is based on a new strategy called pattern …