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Abstract— When data sets have one or more similar characteristics, the clustering in each of these data sets will have an effect on the other data sets. However, for various reasons such as data security issues, these data cannot be stored centrally but in different places. Collaborative clustering is a clustering technique that allows to performance of local clustering on each sub-data set and to exchange of information with other data sets. A collaborative process will be performed to adjust the clustering results on each subset to achieve better clustering results on the subsets of data. This paper presents a collaborative fuzzy clustering approach in big data analysis based on a high-performance computational model to improve the computation speed. Experiments on the Kitsune network attack dataset show that the proposed algorithm significantly improves the calculation speed compared to the previous method.

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