  In this paper we propose a new approach for Big Data mining and analysis.
This new approach works well on distributed datasets and deals with data
clustering task of the analysis. The approach consists of two main phases, the
first phase executes a clustering algorithm on local data, assuming that the
datasets was already distributed among the system processing nodes. The second
phase deals with the local clusters aggregation to generate global clusters.
This approach not only generates local clusters on each processing node in
parallel, but also facilitates the formation of global clusters without prior
knowledge of the number of the clusters, which many partitioning clustering
algorithm require. In this study, this approach was applied on spatial
datasets. The proposed aggregation phase is very efficient and does not involve
the exchange of large amounts of data between the processing nodes. The
experimental results show that the approach has super linear speed up, scales
up very well, and can take advantage of the recent programming models, such as
MapReduce model, as its results are not affected by the types of
communications.
