A survey on condensed representations for frequent sets
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 …
given predicate has been studied extensively the last few years. The specific problem of …
A tutorial on statistically sound pattern discovery
W Hämäläinen, GI Webb - Data Mining and Knowledge Discovery, 2019 - Springer
Statistically sound pattern discovery harnesses the rigour of statistical hypothesis testing to
overcome many of the issues that have hampered standard data mining approaches to …
overcome many of the issues that have hampered standard data mining approaches to …
Mining all non-derivable frequent itemsets
T Calders, B Goethals - Principles of Data Mining and Knowledge …, 2002 - Springer
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 …
improvements. However, if the minimal support threshold is set too low, or the data is highly …
Fast and memory efficient mining of frequent closed itemsets
C Lucchese, S Orlando… - IEEE Transactions on …, 2005 - ieeexplore.ieee.org
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 …
lossless and condensed representation of all the frequent itemsets that can be mined from a …
Free-sets: a condensed representation of boolean data for the approximation of frequency queries
JF Boulicaut, A Bykowski, C Rigotti - Data Mining and Knowledge …, 2003 - Springer
Given a large collection of transactions containing items, a basic common data mining
problem is to extract the so-called frequent itemsets (ie, sets of items appearing in at least a …
problem is to extract the so-called frequent itemsets (ie, sets of items appearing in at least a …
Non-derivable itemset mining
T Calders, B Goethals - Data Mining and Knowledge Discovery, 2007 - Springer
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 …
This principle allows for excluding candidate itemsets from the expensive counting phase. In …
Discovering shared conceptualizations in folksonomies
Social bookmarking tools are rapidly emerging on the Web. In such systems users are
setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared …
setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared …
A condensed representation to find frequent patterns
A Bykowski, C Rigotti - Proceedings of the twentieth ACM SIGMOD …, 2001 - dl.acm.org
Given a large set of data, a common data mining problem is to extract the frequent patterns
occurring in this set. The idea presented in this paper is to extract a condensed …
occurring in this set. The idea presented in this paper is to extract a condensed …
Efficient mining of association rules based on formal concept analysis
Association rules are a popular knowledge discovery technique for warehouse basket
analysis. They indicate which items of the warehouse are frequently bought together. The …
analysis. They indicate which items of the warehouse are frequently bought together. The …
Constraint-based data mining
JF Boulicaut, B Jeudy - Data mining and knowledge discovery handbook, 2010 - Springer
Summary Knowledge Discovery in Databases (KDD) is a complex interactive process. The
promising theoretical framework of inductive databases considers this is essentially a …
promising theoretical framework of inductive databases considers this is essentially a …