  Clustering techniques are very attractive for extracting and identifying
patterns in datasets. However, their application to very large spatial datasets
presents numerous challenges such as high-dimensionality data, heterogeneity,
and high complexity of some algorithms. For instance, some algorithms may have
linear complexity but they require the domain knowledge in order to determine
their input parameters. Distributed clustering techniques constitute a very
good alternative to the big data challenges (e.g.,Volume, Variety, Veracity,
and Velocity). Usually these techniques consist of two phases. The first phase
generates local models or patterns and the second one tends to aggregate the
local results to obtain global models. While the first phase can be executed in
parallel on each site and, therefore, efficient, the aggregation phase is
complex, time consuming and may produce incorrect and ambiguous global clusters
and therefore incorrect models. In this paper we propose a new distributed
clustering approach to deal efficiently with both phases, generation of local
results and generation of global models by aggregation. For the first phase,
our approach is capable of analysing the datasets located in each site using
different clustering techniques. The aggregation phase is designed in such a
way that the final clusters are compact and accurate while the overall process
is efficient in time and memory allocation. For the evaluation, we use two
well-known clustering algorithms, K-Means and DBSCAN. One of the key outputs of
this distributed clustering technique is that the number of global clusters is
dynamic, no need to be fixed in advance. Experimental results show that the
approach is scalable and produces high quality results.
