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Clustering and Nonnegative Matrix Factorization: A Mathematical and Algorithmic Perspective

Year: 2025

Type: article

Abstract: Clustering is a fundamental task in machine learning and data analysis, enabling the discovery of inherent patterns within data. Nonnegative Matrix Factorization (NMF) has emerged as a powerful tool for clustering due to its ability to learn parts-based, interpretable representations. This article explores the theoretical foundations of clustering and NMF, their synergy, algorithmic formulations, and practical implementations. Experimental validation on synthetic data demonstrates the effectiveness of NMF-based clustering without using libraries such as sklearn or tensorflow.

DOI: 10.38124/ijisrt/25jun139

Author: Mitat Uysal

Institution: Doğuş University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4411395887

FWCI:

Cites: 21

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Related to: 10