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Efficient extraction of useful knowledge from very large datasets is still a challenge, mainly when the datasets are distributed, heterogeneous and of different quality depending of the various nodes involved. To reduce the overhead cost due to communications, most of the existing distributed clustering approaches generates global models by aggregating local results obtained on each individual node...
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...
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