Kernel-Based Exponential Grey WOLF Optimizer for Rapid Centroid Estimation in Data Clustering
Amolkumar Narayan Jadhav, Gomathi N
Journal of E - Technology, Vol. 11, No. 1, 2020.
DOI: https://doi.org/10.6025/jet/2020/11/1/9-22
Link: http://www.dline.info/jet/fulltext/v11n1/jetv11n1_2.pdf
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.