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Journal of E-Technology

Kernel-Based Exponential Grey WOLF Optimizer for Rapid Centroid Estimation in Data Clustering
Amolkumar Narayan Jadhav, Gomathi N
Vel-Tech Dr. RR & Dr. SR Technical University India
Abstract: Clustering finds variety of application in a wide range of disciplines because it is mostly helpful for grouping of similar data objects together. Due to the wide applicability, different algorithms have been presented in the literature for segmenting large multidimensional data into discernible representative clusters. Accordingly, in this paper, Kernel-based exponential grey wolf optimizer is developed for rapid centroid estimation in data clustering. Here, a new algorithm, called (EGWO) is newly proposed to search the cluster centroids with a new objective evaluation which considered two parameters called logarithmic kernel function and distance difference between two top clusters. Based on the new objective function and the modified EGWO algorithm, centroids are encoded as position vectors and the optimal location is found for the final clustering. The proposed EGWO algorithm is evaluated with banknote authentication Data Set and iris dataset using four metrics such as, MSE, F-measure, Rand co-efficient and jaccord coefficient. From the outcome, we proved that the proposed EGWO algorithm outperformed the existing PSC, mPSC and GWO algorithm.
Keywords: Clustering, Kernel, Grey wolf optimizer, Optimization, F-measure Kernel-Based Exponential Grey WOLF Optimizer for Rapid Centroid Estimation in Data Clustering
DOI:https://doi.org/10.6025/jet/2020/11/1/9-22
Full_Text   PDF 2006 KB   Download:   390  times
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