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Yves Bastide
Researcher at Blaise Pascal University
Publications - 17
Citations - 4284
Yves Bastide is an academic researcher from Blaise Pascal University. The author has contributed to research in topics: Association rule learning & Formal concept analysis. The author has an hindex of 14, co-authored 17 publications receiving 4195 citations. Previous affiliations of Yves Bastide include French Institute for Research in Computer Science and Automation.
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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.