In this paper, we address the problem of nding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of nding frequent closed itemsets. Based on this statement, we can construct e cient data mining algorithms by limiting the search space to the closed itemset lattice rather than the subset lattice. Moreover, we show that the set of all frequent closed itemsets su ces to determine a reduced set of association rules, thus addressing another important data mining problem: limiting the number of rules produced without information loss. We propose a new algorithm, called A-Close, using a closure mechanism to nd frequent closed itemsets. We realized experiments to compare our approach to the commonly used frequent itemset search approach. Those experiments showed that our approach is very valuable for dense and/or correlated data that represent an important part of existing databases.

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International audienceWe introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data

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In this paper, we propose the algorithm PASCAL which introduces a novel optimization of the well-known algorithm Apriori. This optimization is based on a new strategy called pattern counting inference that relies on the concept of key patterns. We show that the support of frequent non-key patterns can be inferred from frequent key patterns without accessing the database. Experiments comparing PAS-CAL to the three algorithms Apriori, Close and Max-Miner, show that PASCAL is among the most efficient algorithms for mining frequent patterns.

The selectivity of vibrational excitation by electron impact has been used to unambiguously assign the negative ion states (resonances) of chlorobenzene and to settle a recent controversy on this subject. The excitation functions of the ring deformation vibrations exhibit bands in the 0.8-1.4 eV range, identifying them as temporary electron captures in the b 1 and a 2 p* orbitals. A broad band peaking at 2.6 eV appears in the excitation functions of the C-Cl stretch vibration but is missing in the excitation functions of the ring deformation vibrations, proving that it corresponds to a temporary electron capture in the s à C Cl orbital. A more detailed insight into the properties of the potential surfaces of the anion is gained from the excitation functions of many vibrations, and from their comparison with anion potential curves based on the Koopmans theorem. Slopes of the potential curves in the Franck-Condon region reproduce well the observed intensities of totally symmetric vibrations. Strong excitation of out-of-plane vibrations, corroborated by the calculations, reveal vibronic coupling of the b 1 p* and the s* anion states. The a 2 p* state and the s* state are coupled by vibrations with a 2 symmetry. Excitation of in-plane non-totally symmetric vibrations (b 2 ) reveals vibronic coupling between the two p* states b 1 and a 2 , which is also reproduced by the calculated potential curves. The results indicate that symmetry lowering induced by vibronic coupling provides the path for dissociation of the p* states of the chlorobenzene anion.

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