Harris' hawks optimization-tuned density-based clustering

Muhammad Shoaib Omar; Syed Muhammad Waqas; Kashif Talpur; Sumra Khan; Shakeel Ahmad

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Harris' hawks optimization-tuned density-based clustering

Author(s)

Muhammad Shoaib Omar Department of Computer Science, Bahria University,Karachi,Pakistan

Syed Muhammad Waqas Department of Computer Science, Muhammad Ali Jinnah University,Karachi,Pakistan

Kashif Talpur Department of Science and Engineering, Solent University,Southampton,United Kingdom

Sumra Khan Department of Information Technology, Salim Habib University,Karachi,Pakistan

Shakeel Ahmad Department of Science and Engineering, Solent University,Southampton,United Kingdom

Abstract

Clustering is a machine learning technique that groups data samples based on similarity and identifies outliers with distinct features. Density-based clustering outperforms other methods because it can handle arbitrary shapes of clustering distributions. However, it has a limitation of requiring empirical values for the cluster center and the nominal distance between the cluster center and other data points. These values affect the accuracy and the number of clusters obtained by the algorithm. This paper proposes a solution to optimize these parameters using Harris' hawks optimization (HHO), an efficient optimization technique that balances exploration and exploitation and avoids stagnation in later iterations. The proposed HHO-tuned density-based clustering achieves better performance as compared to other optimizers used in this work. This research also provides a reference for designing efficient clustering techniques for complexshaped datasets.

Publication Details

Page(s) 23-34

DOI DOI not available

Published Journal: Sukkur IBA Journal of Emerging Technologies (SJET), Volume: 6, Issue: 1, Year: 2023

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Keywords

Machine learning, clustering, metaheuristic algorithm, density-based clustering, Harris' hawk optimization

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