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 can help businesses make more effective choices is known as big data analysis (BDA). Quick and effective access to this data allows businesses to be flexible in developing strategies to hold onto their competitive edge. To analyze massive amounts of data quickly through parallel processing, the structure of the Hadoop software employs the MapReduce methodology. Computational solid resources are necessary for BDA, although they are not always available. Developing new 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 the clustering 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 also presents a function that calculates the separation among the cluster center and the particular instance, to assist with better clustering. By simulating shared memory space and parallelizing on the framework known as MapReduce on the Hadoop cloud computing platform, the distributed database is utilized to improve the method’s effectiveness while reducing its time complexities. The experimental investigation was conducted on existing approaches, and the proposed approach was analyzed using three standard datasets. While differentiating from existing approaches, the presented approach yields greater performances in terms of various metrics. [ABSTRACT FROM AUTHOR]