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 …

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 …

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 …

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 …

Many mature term-based or pattern-based approaches have been used in the field of
information filtering to generate users' information needs from a collection of documents. A …

Summary Knowledge Discovery in Databases (KDD) is a complex interactive process. The
promising theoretical framework of inductive databases considers this is essentially a …

Association rule mining has contributed to many advances in the area of knowledge
discovery. However, the quality of the discovered association rules is a big concern and has …

Association rules and decision trees represent two well-known data mining techniques to
find predictive rules. In this work, we present a detailed comparison between constrained …

The extraction of frequent patterns often yields extremely voluminous results which are
difficult to handle. Computing a concise representation or cover of the frequent pattern set is …

Patterns are at the core of the discovery of a lot of knowledge from data but their uses are
limited due to their huge number and their mining cost. During the last decade, many works …