Version 1
: Received: 16 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (14:43:31 CEST)
How to cite:
Montgomery, R. M. Overview of Clustering Techniques: From k-Means to Spectral Methods. Preprints2024, 2024101397. https://doi.org/10.20944/preprints202410.1397.v1
Montgomery, R. M. Overview of Clustering Techniques: From k-Means to Spectral Methods. Preprints 2024, 2024101397. https://doi.org/10.20944/preprints202410.1397.v1
Montgomery, R. M. Overview of Clustering Techniques: From k-Means to Spectral Methods. Preprints2024, 2024101397. https://doi.org/10.20944/preprints202410.1397.v1
APA Style
Montgomery, R. M. (2024). Overview of Clustering Techniques: From k-Means to Spectral Methods. Preprints. https://doi.org/10.20944/preprints202410.1397.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "Overview of Clustering Techniques: From k-Means to Spectral Methods" Preprints. https://doi.org/10.20944/preprints202410.1397.v1
Abstract
This article presents an in-depth overview of clustering techniques, which play a vital role in unsupervised learning by uncovering natural groupings in data. We examine five prominent methods: k-Means, k-Medoids, Kohonen Networks and Self-Organizing Maps (SOMs), Fuzzy C-Means, Hierarchical Clustering, and Spectral Clustering. Each technique is described in detail, including its mathematical foundation, operational mechanism, applications, strengths, and limitations. The goal is to provide a thorough understanding of each approach, helping readers select the most appropriate method for their data analysis needs. Practical examples are also provided, demonstrating the application of these clustering techniques in various real-world contexts such as customer segmentation, image processing, and bioinformatics.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
