Classification non supervisée des données hétérogènes à large échelle

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 pro...

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Bibliographic Details
Published in:arXiv.org 2017-07
Main Authors: Mohamed Ali Zoghlami, Arfaoui, Olfa, Minyar Sassi Hidri, Rahma Ben Ayed
Format: Article
Language:English
Subjects:
Clustering
Qualitative analysis
Response time
Online Access:Get full text
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Summary: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.
ISSN:2331-8422