Characterizing approximate-matching dependencies in formal concept analysis with pattern structures

Functional dependencies (FDs) provide valuable knowledge on the relations between attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It has been shown that FDs can be expressed in terms of partitions of tuples that are in agreement w.r.t. the values taken by some subsets of attributes. To extend the use of FDs, several generalizations have been proposed. In this work, we study approximatematching dependencies that generalize FDs by relaxing the constraints on the attributes, i.e. agreement is based on a similarity relation rather than on equality. Such dependencies are attracting attention in the database field since they allow uncrisping the basic notion of FDs extending its application to many different fields, such as data quality, data mining, behavior analysis, data cleaning or data partition, among others. We show that these dependencies can be formalized in the framework of Formal Concept Analysis (FCA) using a previous formalization introduced for standard FDs. Our new results state that, starting from the conceptual structure of a pattern structure, and generalizing the notion of relation between tuples, approximate-matching dependencies can be characterized as implications in a pattern concept lattice. We finally show how to use basic FCA algorithms to construct a pattern concept lattice that entails these dependencies after a slight and tractable binarization of the original data.Postprint (author's final draft

PGLCM: Efficient Parallel Mining of Closed Frequent Gradual Itemsets

International audienceNumerical data (e.g., DNA micro-array data, sensor data) pose a challenging problem to existing frequent pattern mining methods which hardly handle them. In this framework, gradual patterns have been recently proposed to extract covariations of attributes, such as: "When X increases, Y decreases". There exist some algorithms for mining frequent gradual patterns, but they cannot scale to real-world databases. We present in this paper GLCM, the first algorithm for mining closed frequent gradual patterns, which proposes strong complexity guarantees: the mining time is linear with the number of closed frequent gradual item sets. Our experimental study shows that GLCM is two orders of magnitude faster than the state of the art, with a constant low memory usage. We also present PGLCM, a parallelization of GLCM capable of exploiting multicore processors, with good scale-up properties on complex datasets. These algorithms are the first algorithms capable of mining large real world datasets to discover gradual patterns

Mining Closed Gradual Patterns

With the steady development of the computing tools, we attended last three decades a considerable increase of the quantity of data stored in databases. So, extracting knowledge from this data is of paramount importance. Data mining is becoming an inescapable tool to reach this goal. Association rule extraction is on

Combining multi-variate palaeoecological indicators and mining closed gradual patterns for refining past lake dynamics and the induced ecological legacies

International audienceThe overloading of water bodies with nutrients is a widespread ecological alteration. Arecent synthesis (CNRS, IFREMER, INRA, IRSTEA) shows that lake ecosystems are partic-ularly concerned by the eutrophication process which results in: (1) ecological disturbances(e.g. invasive species, loss of biodiversity, algal and cyanobacterial blooms), and (2) pub-lic health problems (e.g. water quality degradation due to cyanotoxin production). Thismakes the restoration of eutrophic freshwaters an environmental top-priority and a key-issuefor scientic research. Accurate assessments of the present-day lake ecosystems state arethus needed. Moreover, long-term retrospective models of lake dynamics must be developedconcomitantly to the analysis of natural and anthropogenic modications of the catchment,which enhance the external input of sediment and nutrients. Palaeoecological researches aretherefore required because they characterize through time the lake’s responses to cumula-tive changes caused by natural (e.g. climate) and anthropogenic impacts (e.g. vegetationclearance, agriculture). They furnish thus fresh insights into a good understanding of theecological legacies which also determine current lake structure and function, and which mustbe considered for sustainable lake management. A palaeoecological research was conductedin the hyper-eutrophic Lake Aydat (837 m a.s.l.), located in the Chaˆıne des Puys (FrenchMassif Central). The high resolution and multi-proxy analysis (pollen, non-pollen paly-nomophs, diatoms, sedimentology) was combined to an approach of data mining (extractionof frequent (closed) gradual patterns of multi-variate indicators under temporal constraint)and allow to address the:(1) reference conditions of the lake (prior to extensive human impact) and its natural vari-ability;(2) aquatic changes phases: timing, intensity, frequency, delay;(3) reversibility or directions in which the lake is driven by the long-term cumulative impact(e.g. loss of resilience, ratchet effect);(4) potential drivers: climatevsthe diverse range of land uses;(5) degree of resistance/sensitivity and vulnerability of present-day lake ecosytems