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

Dandugala, Lakshmi Srinivasulu; Vani, Koneru Suvarna

doi:10.22111/ijfs.2024.44621.7855

Document Type : Research Paper

Authors

¹ Research Scholar, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India

² Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Kanuru, Vijayawada, Andhra Pradesh.

https://doi.org/10.22111/ijfs.2024.44621.7855

Abstract

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.

Keywords

Main Subjects

Computer Science

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

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Volume 21, Issue 2March and April 2024Pages 141-160

Volume 21, Issue 2
March and April 2024
Pages 141-160