Advanced Big Data Analytics: Integrating Fuzzy C-Means, Encoder-Decoder CNNs, and Genetic Algorithms for Efficient Clustering and Classification

Abstract

In the realm of Big Data analysis, the pivotal question of data clustering takes center stage. This study delves into optimizing this analysis by adopting a hybrid approach that integrates the Fuzzy C-Means (FCM) methodology, Encoder-Decoder Convolutional Neural Networks (CNN), Genetic Algorithms (GAs), and an optimal classification strategy for data clustering and categorization. FCM provides a flexible clustering foundation with its fuzzy logic, while the Encoder-Decoder CNN contributes to extracting complex features and refining the model. Genetic Algorithms finely adjust the parameters of the hybrid model. The optimal classification strategy complements this approach by ensuring precise data categorization. This hybrid strategy leverages the specific strengths of each component, thereby overcoming inherent limitations in each technique. FCM ensures robust cluster formation the Encoder-Decoder CNN improves feature representation, Genetic Algorithms optimize the hyper-parameters of the hybrid model, and optimal classification reinforces the accuracy of data categorization. Experiments conducted on various Big Data sets reveal a significant enhancement in clustering and classification accuracy, as well as overall analysis efficiency. This research represents a substantial contribution to the evolution of Big Data analysis by proposing an integrated solution harnessing the power of FCM, Encoder-Decoder CNN, Genetic Algorithms, and optimal classification The results suggest that this hybrid approach not only increases clustering and classification accuracy but also provides a versatile and adaptable solution to address challenges in large-scale data analysis.