Treffer: Deep Fuzzy Clustering-based Fractional Crayfish Optimization for big data clustering in MapReduce approach.

This paper proposes the Fractional Crayfish Optimization Algorithm_Deep Fuzzy Clustering (FCOA_DFC) model to enhance both clustering accuracy and computational efficiency. At first, input big data is applied to a MapReduce framework. The MapReduce framework includes the mapper and the reducer phases. The mapper phase contains numerous mappers and every mapper performs the data normalization utilizing linear normalization technique. Feature selection is established utilizing the hybrid feature selection model. The result of all mappers is forwarded to the reducer phase, wherein the overall features are merged and subjected to a data clustering process, where clustering is performed by DFC. The parameters of the DFC are trained by FCOA, which is developed by the integration of Fractional Calculus (FC) and Crayfish Optimization Algorithm (COA). Moreover, the proposed FCOA-DFC model achieved a Jaccard coefficient of 0.928, clustering accuracy of 0.948, and random coefficient of 0.928, demonstrating its superior performance compared to the existing methods. [ABSTRACT FROM AUTHOR]

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