Paper Info
Title
Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat Optimization and Sparse Fuzzy C-Means.
Abstract
The technical advancements in big data have become popular and most desirable among users for storing, processing, and handling huge data sets. However, clustering using these big data sets has become a major challenge in big data analysis. The conventional clustering algorithms used scalable solutions for managing huge data sets. Thus, this study proposes a technique for big data clustering using the spark architecture. The proposed technique undergoes two steps for clustering the big data, involving feature selection and clustering, performed in the initial cluster nodes of spark architecture. At first, the initial cluster nodes read the big data from various distributed systems, and the optimal features are selected and placed in the feature vector based on the proposed moth-flame optimization-based bat (MFO-Bat) algorithm, which is designed by integrating MFO and Bat algorithms. Then, the selected features are fed to the final cluster nodes of spark, which uses the sparse-fuzzy C-means method for performing optimal clustering. The performance of proposed MFO-Bat outperformed other existing methods with a maximal classification accuracy of 95.806%, Dice coefficient of 99.181%, and Jaccard coefficient of 98.376%, respectively.
Year
DOI
Venue
2020
10.1089/big.2019.0125
BIG DATA
Keywords
DocType
Volume
big data,big data clustering,fuzzy,optimization algorithm,spark architecture
Journal
8
Issue
ISSN
Citations 
3
2167-6461
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Vasavi Ravuri100.34
S Vasundra200.34