Classification non supervis\'ee des donn\'ees h\'et\'erog\`enes \`a large \'echelle
Databases
2017-07-04 v1 Machine Learning
Abstract
When it comes to cluster massive data, response time, disk access and quality of formed classes becoming major issues for companies. It is in this context that we have come to define a clustering framework for large scale heterogeneous data that contributes to the resolution of these issues. The proposed framework is based on, firstly, the descriptive analysis based on MCA, and secondly, the MapReduce paradigm in a large scale environment. The results are encouraging and prove the efficiency of the hybrid deployment on response quality and time component as on qualitative and quantitative data.
Keywords
Cite
@article{arxiv.1707.00297,
title = {Classification non supervis\'ee des donn\'ees h\'et\'erog\`enes \`a large \'echelle},
author = {Mohamed Ali Zoghlami and Olfa Arfaoui and Minyar Sassi Hidri and Rahma Ben Ayed},
journal= {arXiv preprint arXiv:1707.00297},
year = {2017}
}
Comments
6 pages, in French, 8 figures