Advancing big data clustering with fuzzy logic-based IMV-FCA and ensemble approach

The act of gathering, looking over, and analyzing a lot of data to find patterns, insights, and market trends that canhelp businesses make more effective choices is known as big data analysis (BDA). Quick and effective access to thisdata allows businesses to be flexible in developing strategies to hold onto their competitive edge. To analyze massiveamounts of data quickly through parallel processing, the structure of the Hadoop software employs the MapReducemethodology. Computational solid resources are necessary for BDA, although they are not always available. Developingnew clustering techniques that could handle this kind of data processing became crucial. Therefore, in this research,we presented a novel, effective fuzzy-based Improved Multiview Fuzzy C-Means Algorithm (IMV-FCA) to boost theclustering strategy. To summarize, fuzzy-based IMV-FCA clustering presents the ensemble of the MobileNet V2 model,and three-layered stacked Bidirectional LSTM (MVSBiLSTM) to increase computing speed and effectiveness. It alsopresents a function that calculates the separation among the cluster center and the particular instance, to assist withbetter clustering. By simulating shared memory space and parallelizing on the framework known as MapReduce onthe Hadoop cloud computing platform, the distributed database is utilized to improve the method’s effectiveness whilereducing its time complexities. The experimental investigation was conducted on existing approaches, and the proposedapproach was analyzed using three standard datasets. While differentiating from existing approaches, the presentedapproach yields greater performances in terms of various metrics.