Mickaël Fabrègue

Mining closed partially ordered patterns, a new optimized algorithm

Nowadays, sequence databases are available in several domains with increasing sizes. Exploring such databases with new pattern mining approaches involving new data structures is thus important. This paper investigates this data mining challenge by presenting OrderSpan, an algorithm that is able to extract a set of closed partially ordered patterns from a sequence database. It combines well-known properties of prefixes and suffixes. Furthermore, we extend OrderSpan by adapting efficient optimizations used in sequential pattern mining domain. Indeed, the proposed method is flexible and follows the sequential pattern paradigm. It is more efficient in the search space exploration, as it skips redundant branches. Experiments were performed on different real datasets to show (1) the effectiveness of the optimized approach and (2) the benefit of closed partially ordered patterns with respect to closed sequential patterns.

OrderSpan: Mining Closed Partially Ordered Patterns

Due to the complexity of the task, partially ordered pattern mining of sequential data has not been subject to much study, despite its usefulness. This paper investigates this data mining challenge by describing OrderSpan, a new algorithm that extracts such patterns from sequential databases and overcomes some of the drawbacks of existing methods. Our work consists in providing a simple and flexible framework to directly mine complex sequences of itemsets, by combining well-known properties on prefixes and suffixes. Experiments were performed on different real datasets to show the benefit of partially ordered patterns.

Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment

We propose a new data mining process to extract original knowledge from hydro-ecological data, in order to help the identification of pollution sources. This approach is based (1) on a domain knowledge discretization (quality classes) of physico-chemical and biological parameters, and (2) on an extraction of temporal patterns used as discriminant features to link physico-chemistry with biology in river sampling sites. For each bio-index quality value, we obtained a set of significant discriminant features. We used them to identify the physico-chemical characteristics that impact on different biological dimensions according to their presence in extracted knowledge. The experiments meet with the domain knowledge and also highlight significant mismatches between physico-chemical and biological quality classes. Then, we discuss about the interest of using discriminant temporal patterns for the exploration and the analysis of temporal environmental data such as hydro-ecological databases.

Un système décisionnel pour l’analyse de la qualité des eaux de rivières

Cet article decrit un systeme decisionnel developpe pour permettre l’analyse des donnees concernant le fonctionnement des hydro-ecosystemes ; ces donnees sont nombreuses, diverses et issues de sources variees. Le systeme mis en place comporte une base de donnees integree, un entrepot permettant l’exploration des dimensions associees aux donnees, et des outils de fouille permettant de repondre aux questions des hydro-ecologues.