Data Clustering Using Moth-Flame Optimization Algorithm

Singh, Tribhuvan; Saxena, Nitin; Khurana, Manju; Singh, Dilbag; Abdalla, Mohamed; Alshazly, Hammam

doi:10.3390/s21124086

Open AccessArticle

Data Clustering Using Moth-Flame Optimization Algorithm

by

Tribhuvan Singh

¹

,

Nitin Saxena

²,

Manju Khurana

²

,

Dilbag Singh

³

,

Mohamed Abdalla

^4,5

and

Hammam Alshazly

^6,*

¹

Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India

²

Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India

³

School of Engineering and Applied Sciences, Bennett University, Greater Noida 201310, India

⁴

Department of Mathematics, Faculty of Science, King Khalid University, Abha 62529, Saudi Arabia

⁵

Department of Mathematics, Faculty of Science, South Valley University, Qena 83523, Egypt

⁶

Faculty of Computers and Information, South Valley University, Qena 83523, Egypt

^*

Author to whom correspondence should be addressed.

Sensors 2021, 21(12), 4086; https://doi.org/10.3390/s21124086

Submission received: 13 May 2021 / Revised: 10 June 2021 / Accepted: 10 June 2021 / Published: 14 June 2021

(This article belongs to the Special Issue Machine and Deep Learning in Sensing and Imaging: Emerging Trends, Challenges and Opportunities)

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Abstract

A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.

Keywords: data clustering; data mining; k-means; moth flame optimization; meta-heuristic

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MDPI and ACS Style

Singh, T.; Saxena, N.; Khurana, M.; Singh, D.; Abdalla, M.; Alshazly, H. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors 2021, 21, 4086. https://doi.org/10.3390/s21124086

AMA Style

Singh T, Saxena N, Khurana M, Singh D, Abdalla M, Alshazly H. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors. 2021; 21(12):4086. https://doi.org/10.3390/s21124086

Chicago/Turabian Style

Singh, Tribhuvan, Nitin Saxena, Manju Khurana, Dilbag Singh, Mohamed Abdalla, and Hammam Alshazly. 2021. "Data Clustering Using Moth-Flame Optimization Algorithm" Sensors 21, no. 12: 4086. https://doi.org/10.3390/s21124086

APA Style

Singh, T., Saxena, N., Khurana, M., Singh, D., Abdalla, M., & Alshazly, H. (2021). Data Clustering Using Moth-Flame Optimization Algorithm. Sensors, 21(12), 4086. https://doi.org/10.3390/s21124086

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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