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Nowadays, large bodies of data in different domains are collected and stored. An efficient extraction of useful knowledge from these data becomes a huge challenge. This leads to the need for developing distributed data mining techniques. However, only a few research concerns distributed clustering for analysing large, heterogeneous and distributed datasets. Besides, current distributed clustering...
Nowadays the data collections are huge and in most cases do not reside in a centralised location. The latter complicates the task of traditional data mining techniques, as datasets are distributed and often heterogeneous. In this paper we propose a distributed approach based on the aggregation of models produced locally. The datasets will be processed locally on each node to produce clusters from...
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