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A novel data clustering approach based on whale optimization algorithm
Author(s) -
Singh Tribhuvan
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12657
Subject(s) - computer science , cluster analysis , centroid , benchmark (surveying) , data mining , partition (number theory) , optimization algorithm , cluster (spacecraft) , algorithm , position (finance) , artificial intelligence , pattern recognition (psychology) , mathematical optimization , mathematics , geodesy , finance , combinatorics , economics , programming language , geography
Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra‐cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches, the problem of local entrapment is very common due to weak exploration mechanism. In this paper, a novel approach based on a whale optimization algorithm (WOA) is suggested for data clustering. The performance of the suggested approach is validated using 14 benchmark datasets of the UCI machine learning repository. The experimental results and various statistical tests have justified the efficacy of the suggested approach.