  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.
