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In this work we propose an approach of multi-criteria decision making (MCDM) using Elimination Et Choice Transiting Reality (ELECTRE) methods, interval-valued intuitionistic fuzzy (IVIF) sets and possibility theory. The proposition concerns the computation of concordance sets and discordance sets using possibility measures. The proposed approach is applied to select the best investment projects decision...
Data clustering is an important step which evolves in many pattern recognition problems and decision making applications. This step had gained great interest and several approaches were proposed to improve the clustering quality. In this context, we proposed a new ensemble clustering system based on the use of a dynamic fuzzy exponent within fuzzy C-Means clustering, an unsupervised feature selection...
Nowadays, there are many challenges for the logistics industry mainly with the integration of E-commerce and new sources of data such as smartphones, sensors, GPS and other devices. Those new data sources generate daily a huge quantity of unstructured data, to deal with such complex data, the use of big data analytic tools becomes an obligation. In this context, many works have been done recently...
Text detection is a basic step in many computer vision applications including video Optical Character Recognition (OCR), video indexation, understanding video content, etc. Actually, several sources such as mobile devices, monitoring cameras and social networks are generating every day billions of videos with different formats and uncertainty. Such videos require new methods to apply text detection...
In this report, we propose to give a review of the most used clustering methods in the literature. First, we give an introduction about clustering methods, how they work and their main challenges. Second, we present the clustering methods with some comparisons including mainly the classical partitioning clustering methods like well-known k-means algorithms, Gaussian Mixture Modals and their variants,...
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