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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.
Tribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors 2021, 21, 4086 .
AMA StyleTribhuvan Singh, Nitin Saxena, Manju Khurana, Dilbag Singh, Mohamed Abdalla, Hammam Alshazly. Data Clustering Using Moth-Flame Optimization Algorithm. Sensors. 2021; 21 (12):4086.
Chicago/Turabian StyleTribhuvan Singh; Nitin Saxena; Manju Khurana; Dilbag Singh; Mohamed Abdalla; Hammam Alshazly. 2021. "Data Clustering Using Moth-Flame Optimization Algorithm." Sensors 21, no. 12: 4086.
Data clustering is a prevalent problem that belongs to the data mining domain. It aims to partition the given data objects into some specified number of clusters based on the sum of the intra-cluster distances. It is an NP-hard problem, and many heuristic approaches have already been proposed to target the desired objective. However, during the search process, the problem of local entrapment is prevalent due to nonlinear objective functions and a large range of search domains. In this paper, an opposition learning and chaotic sequence guided approaches are incorporated in a fast converging evolutionary algorithm called improved environmental adaptation method with real parameter (IEAM-R) for solving the data clustering problem. A chaotic sequence generated by a sinusoidal chaotic map has been utilized to target promising solutions in the search domain. On the other hand, the inclusion of the opposition learning-based approach allows the solutions to explore more appropriate locations in the search domain. The performance of the proposed approach is compared against some well-known algorithms using fitness values, statistical values, convergence curves, and box plots. These comparisons justify the efficacy of the suggested approach.
Tribhuvan Singh; Nitin Saxena. Chaotic sequence and opposition learning guided approach for data clustering. Pattern Analysis and Applications 2021, 1 -15.
AMA StyleTribhuvan Singh, Nitin Saxena. Chaotic sequence and opposition learning guided approach for data clustering. Pattern Analysis and Applications. 2021; ():1-15.
Chicago/Turabian StyleTribhuvan Singh; Nitin Saxena. 2021. "Chaotic sequence and opposition learning guided approach for data clustering." Pattern Analysis and Applications , no. : 1-15.