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Moth-Flame Optimization-Bat Optimization: Map-Reduce Framework for Big Data Clustering Using the Moth-Flame Bat

Vasavi Ravuri¹, S Vasundra²

¹VNRVJIET, Hyderabad, India.

|May 21, 2020

Summary

This summary is machine-generated.

This study introduces a novel big data clustering technique using Spark architecture. The MFO-Bat algorithm enhances feature selection, leading to superior clustering accuracy and performance compared to existing methods.

Keywords:

big data big data clustering fuzzy optimization algorithm spark architecture

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Big data technologies are crucial for managing large datasets.
Clustering big data presents significant challenges for conventional algorithms.
Scalable solutions are needed for efficient big data analysis.

Purpose of the Study:

To propose an effective big data clustering technique using Spark architecture.
To enhance feature selection and clustering accuracy in big data analysis.
To address the limitations of traditional clustering algorithms for large datasets.

Main Methods:

A two-step clustering process on Spark architecture: feature selection and clustering.
Utilizing the proposed Moth-Flame Optimization-based Bat (MFO-Bat) algorithm for optimal feature selection.
Employing the sparse-fuzzy C-means method for clustering on selected features.

Main Results:

The MFO-Bat algorithm achieved maximal classification accuracy of 95.806%.
The proposed method demonstrated high performance with a Dice coefficient of 99.181% and Jaccard coefficient of 98.376%.
Outperformed existing big data clustering methods.

Conclusions:

The proposed Spark-based technique effectively clusters big data.
The MFO-Bat algorithm significantly improves feature selection for clustering.
This approach offers a scalable and accurate solution for big data clustering challenges.