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MinD research group aims at developping algorithms for data mining and machine learning with a focus on large-scale. In particular, MinD has an expertise on Concept Lattices, Evolutionary Computation, Multi-Agent Systems, Naïve Bayes, Random Forests, Support Vector Machines, … Those methods are used to extract knowledge from Big Data for :

- Association rule learning
- Classification
- Clustering
- Optimization
- Prediction
- Regression
- …

Concepts lattices are theoretical structures defined according to the Galois connection of a finite binary relation. Given a set of instances (objects) described by a list of properties (variables values), the concept lattice is a hierarchy of concepts in which each concept associates a set of instances (extent) sharing the same value for a certain set of properties (intent). Concepts are partially ordered in the lattice according to the inclusion relation: Each sub-concept in the lattice contains a subset of the instances and a superset of the properties in the related concepts above it. In data mining, concept lattices serve as theoretical frameworks for the efficient extraction of non-redundant loss-less condensed representations of association rules and hierarchical biclustering.

Evolutionary Algorithms (EA) are nature inspired and stochastic algorithms that mimic Darwin theory for problem optimization. The particularity of EA is its capacity to deal with multi objectives (i.e. maximizing profits while minimizing costs), multi-modality (several best solutions) as the algorithm considers a population of solutions, discrete or continous optimization, dynamic optimization and many others fundamental problems… As data mining deals now with Big Data, it is natural to consider EA for optimizing models (neural network, association rules, decision trees or SVM…) produced by a mining or a learning process. They can also be considered for hybridation.