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Nicolas Pasquier

Researcher at Centre national de la recherche scientifique

Publications -  55
Citations -  4576

Nicolas Pasquier is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Association rule learning & Cluster analysis. The author has an hindex of 17, co-authored 55 publications receiving 4469 citations. Previous affiliations of Nicolas Pasquier include Blaise Pascal University & University of Nice Sophia Antipolis.

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Book ChapterDOI

Discovering Frequent Closed Itemsets for Association Rules

TL;DR: This paper proposes a new algorithm, called A-Close, using a closure mechanism to find frequent closed itemsets, and shows that this approach is very valuable for dense and/or correlated data that represent an important part of existing databases.
Journal ArticleDOI

Efficient mining of association rules using closed itemset lattices

TL;DR: Experiments showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.
Journal ArticleDOI

Computing iceberg concept lattices with TITANIC

TL;DR: A new algorithm called TITANIC for computing (iceberg) concept lattices is presented, based on data mining techniques with a level-wise approach, and shows an important gain in efficiency, especially for weakly correlated data.
Journal ArticleDOI

Mining frequent patterns with counting inference

TL;DR: It is shown that the support of frequent non-key patterns can be inferred from frequent key patterns without accessing the database, and PASCAL is among the most efficient algorithms for mining frequent patterns.
Book ChapterDOI

Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets

TL;DR: In this article, the authors define two new bases for association rules which union is a generating set for all valid association rules with support and confidence, which consist of the nonredundant exact and approximate association rules having minimal antecedents and maximal consequents.