Bigdata clustering and classification with improved fuzzy based deep architecture under MapReduce framework

• Published in: Intelligent decision technologies Vol. 18; no. 2; pp. 1511 - 1540
• Main Authors: D, Vishnu Sakthi, V, Valarmathi, V, Surya, A, Karthikeyan, E, Malathi
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2024; Sage Publications Ltd
• Subjects: Big Data; Classification; Clustering; Performance measurement; improved feature fusion; normalization; fuzzy C-means clustering; Map reduce framework
• ISSN: 1872-4981; 1875-8843
• DOI: 10.3233/IDT-230537

The current state of economic, social ideas, and the advancement of cutting-edge technology are determined by the primary subjects of the contemporary information era, big data. People are immersed in a world of information, guided by the abundance of data that penetrates every element of their surroundings. Smart gadgets, the IoT, and other technologies are responsible for the data’s explosive expansion. Organisations have struggled to store data effectively throughout the past few decades. This disadvantage is related to outdated, expensive, and inadequately large storage technology. In the meanwhile, large data demands innovative storage techniques supported by strong technology. This paper proposes the bigdata clustering and classification model with improved fuzzy-based Deep Architecture under the Map Reduce framework. At first, the pre-processing phase involves data partitioning from the big dataset utilizing an improved C-Means clustering procedure. The pre-processed big data is then handled by the Map Reduce framework, which involves the mapper and reducer phases. In the mapper phase. Data normalization takes place, followed by the feature fusion approach that combines the extracted features like entropy-based features and correlation-based features. In the reduction phase, all the mappers are combined to produce an acceptable feature. Finally, a deep hybrid model, which is the combination of a DCNN and Bi-GRU is used for the classification process. The Improved score level fusion procedure is used in this case to obtain the final classification result. Moreover, the analysis of the proposed work has proved to be efficient in terms of classification accuracy, precision, recall, FNR, FPR, and other performance metrics.