Data Clustering Using Moth-Flame Optimization Algorithm

Sensors

Tribhuvan Singh¹  Manju Khurana²  Nitin Saxena²  Mohamed Abdalla³  Hammam Alshazly⁴  Dilbag Singh⁵ 

[1] Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, India;Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India;Department of Mathematics, Faculty of Science, King Khalid University, Abha 62529, Saudi Arabia;Faculty of Computers and Information, South Valley University, Qena 83523, Egypt;School of Engineering and Applied Sciences, Bennett University, Greater Noida 201310, India;

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

DOI  :  10.3390/s21124086

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown