Constrained frequent patterns and closed frequent patterns are two paradigms aimed at reducing the set of extracted patterns
to a smaller, more interesting, subset. Although a lot of work has been done with both these paradigms, there is still confusion
around the mining problem obtained by joining closed and constrained frequent patterns in a unique framework. In this paper,
we shed light on this problem by providing a formal definition and a thorough characterisation. We also study computational
issues and show how to combine the most recent results in both paradigms, providing a very efficient algorithm that exploits
the two requirements (satisfying constraints and being closed) together at mining time in order to reduce the computation
as much as possible.
Keywords Closed frequent itemsets - Condensed representations - Constraints - Frequent itemsets mining
Francesco Bonchi received his Ph.D. in computer science from the University of Pisa in December 2003, with the thesis “Frequent Pattern Queries:
Language and Optimizations”. Currently, he is a postdoc at the Institute of Information Science and Technologies (ISTI) of
the Italian National Research Council in Pisa, where he is a member of the Knowledge Discovery and Delivery Laboratory. He
has been a visiting fellow at the Kanwal Rekhi School of Information Technology, Indian Institute of Technology, Bombay (2000,
2001). His current research interests are data mining query language and Optimization, frequent pattern mining, privacy-preserving
data mining, bioinformatics. He is one of the teachers of a course on data mining held at the faculty of Economics at the
University of Pisa. He served as a referee at various national and international conferences on databases, data mining, logic
programming and artificial intelligence.
Claudio Lucchese received the Master Degree in Computer Science summa cum laude from Ca' Foscari University of Venice in October 2003. He is currently a Ph.D. student at the same university and Research
Associate at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council in Pisa,
where he is a member of the High Performance Computing Laboratory. He is mainly interested in frequent pattern mining, privacy-preserving
data mining, and data mining techniques for information retrieval.