A cloud-based optimal fuzzy clustering of distributed data

Cloud computing is an infrastructure that allows the storage of large datasets. It provides a great and parallel computing which permits a faster computation on distributed data. The contribution of this paper concerns the development of a cloud-based fuzzy clustering algorithm of distributed datasets while detecting the optimal partition in a global view. The proposed algorithm meets the confidentiality constraint which prohibits the sharing of data between different resources while guaranteeing the data anonymity located on the cloud servers. A series of experiments was conducted to evaluate the efficiency of the proposed algorithm. The obtained results show the performance of the proposed algorithm on both quality and response time components.

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