HMOSHSSA: a novel framework for solving simultaneous clustering and feature selection problems

Title

HMOSHSSA: a novel framework for solving simultaneous clustering and feature selection problems

Creator

Kumar V.; Kumari R.; Kumar S.

Description

In real-life scenarios, information about the number of clusters is unknown. Due to this, clustering algorithms are unable to generate the valuable partitions. Beside this, the appropriate and optimal number of features is also required to produce the good quality clusters. The selection of optimal number of clusters and feature is a challenging task in the clustering. To resolve these problems, an automatic multi-objective-based clustering approach called HMOSHSSA is proposed in this paper. In HMOSHSSA, the spotted hyena and salp swarm algorithms are hybridized to obtain a better trade-off between these algorithms intensification and diversification capabilities. Two novel concepts for encoding and threshold setting are incorporated in the HMOSHSSA. The encoding scheme is used to choose the optimal number of clusters and features during the optimization process. The variance of dataset is used for setting the threshold values for both clusters and features. A novel fitness function is proposed to improve the optimization process. The suggested algorithms performance is evaluated using eight well-known real-world datasets. The statistical significance of HMOSHSSA is measured through t-tests. Results reveal that the proposed approach is able to detect the optimal number of clusters and features from a given dataset without user intervention. This approach is also deployed for solving microarray data analysis and image segmentation problems. HMOSHSSA outperformed the other considered algorithms in terms of performance measures. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Source

Multimedia Tools and Applications, Vol-83, No. 35, pp. 82149-82175.

Date

2024-01-01

Publisher

Springer

Subject

Dimensionality reduction; Feature selection; Partitional clustering; Pattern recognition; Salp swarm algorithm; Spotted hyena optimizer

Coverage

Kumar V., Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, 147008, India; Kumari R., ICFAI Business School (IBS) Bangalore, Off-Campus Center of ICFAI Foundation for Higher Education (IFHE) University, Bangalore, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India

Rights

Restricted Access

Relation

ISSN: 13807501; CODEN: MTAPF

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s11042-024-18726-7

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187449131&doi=10.1007%2fs11042-024-18726-7&partnerID=40&md5=c7b33650edc0f66266fa0ac775395580