Current research projects concern the development of novel and innovative algorithmic approaches for the extraction of knowledge patterns and models from massive data and their application to biodiversity, bioinformatics, travel industry, and web usage data.
Objective: Combine clustering results in order to identify clustering patterns agreed by different numbers of clustering, using frequent closed patterns, and generate new consensus clustering solutions. These consensus clustering solutions are then presented in a tree structure that allows the end-user to better understand the properties of the data space, by identifying strong clusters and unstable instances from the viewpoint of clustering, and determine the best consensus solution according to his/her prior knowledge of the application.
Methods: Frequent closed pattern mining; R Language
Collaborators: Atheer Al-Najdi (Ministry of Higher Education and Scientific Research, Bagdad, Iraq), Karell Bertet (Lab. L3i, Univ. La Rochelle, France), Frederic Precioso (Lab. I3S, Univ. Nice Sophia-Antipolis), Arnaud Revel (Lab. L3i, Univ. La Rochelle, France).
Objective: Develop and implement a theroretical framework for the integration of heterogeneous biodiversity data, stored in different formats and originating from different sources, with related formal and tacit background knowledge for the extraction of knowledge patterns and models. From the resulting information repository, different specific date mining contexts are generated, according to the objectives of the biodiversity analytical application, and processed to extract biodiversity knowledge patterns and models.
Methods: Information integration; Ontologies; Knowledge extraction approaches
Collaborators: Somsack Inthasone (National Univ. of Laos, Vientiane, Laos), Celia da Costa Pereira (Lab. I3S, Univ. Nice Sophia-Antipolis), Andrea Tettamanzi (Lab. I3S, Univ. Nice Sophia-Antipolis)