ItemConference Paper

Approaches To Improve Performance of K-Means Clustering

Title
Approaches To Improve Performance of K-Means Clustering
Creator
Kumar, Prince; Sharma, Nandini; Bushra, Seema; Chinnaiyan, R.; Khanna, Vimal Kumar; Sabarmathi, G.
Description
In this research, we present an enhanced K-Means clustering approach utilizing Neural Engine processors integrated within distributed smartphone networks. Each smartphone runs the K-Means algorithm locally using its Neural Engine to compute centroids efficiently, and these local centroids are then combined to form global clusters on a cloud server. Our implementation significantly reduces computation time while maintaining high clustering accuracy. Experimental evaluation on large datasets demonstrates improved performance over traditional K-Means, proving its suitability for big data analytics in healthcare, IoT, and smart mobile applications. This approach ensures faster processing, lower energy consumption, and effective resource utilization within distributed environments. Further, the proposed method addresses challenges in data privacy by performing local computation and only sharing centroid information. The results indicate potential for scalable clustering solutions in real-time scenarios, opening new directions for edge-cloud integrated machine learning frameworks that harness device-level AI accelerators for complex data-driven tasks efficiently. 2025 IEEE.
Source
2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Genetic Algorithm; K Means Clustering; KM-SML; Neural Engine Processor; Principal Components Analysis
Coverage
Kumar P., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sharma N., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Bushra S., AI/ML Lab, Centre of Excellence, School of Commerce & Management, Lingaya's Vidyapeeth, Haryana, Faridabad, India; Chinnaiyan R., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Khanna V.K., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sabarmathi G., School of Computer Science, Christ University, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159610-1;
Format
online
Language
English
Type
Conference paper

Citation

Kumar, Prince; Sharma, Nandini; Bushra, Seema; Chinnaiyan, R.; Khanna, Vimal Kumar; Sabarmathi, G., “Approaches To Improve Performance of K-Means Clustering,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25918.

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