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 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 …

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 …

Association rule extraction from operational datasets often produces several tens of
thousands, and even millions, of association rules. Moreover, many of these rules are …

Association rules are a data mining technique used to discover frequent patterns in a data
set. In this work, association rules are used in the medical domain, where data sets are …

Association rules represent a promising technique to find hidden patterns in a medical data
set. The main issue about mining association rules in a medical data set is the large number …

Abstract Formal Concept Analysis is an unsupervised learning technique for conceptual
clustering. We introduce the notion of iceberg concept lattices and show their use in …

Closed sets have been proven successful in the context of compacted data representation
for association rule learning. However, their use is mainly descriptive, dealing only with …

Galois (concept) lattice theory has been successfully applied in data mining for the
resolution of the association rule problem. In particular, structural results about lattices have …

Constrained pattern mining extracts patterns based on their individual merit. Usually this
results in far more patterns than a human expert or a machine learning technique could …