Clustering: finding patterns in the darkness

Menéndez, Héctor D. ORCID logoORCID: https://orcid.org/0000-0002-6314-3725 (2021) Clustering: finding patterns in the darkness. Open Journal of Machine Learning, 1 (1) . pp. 1-28. [Article] (doi:10.46723/ojml.v1i1.4)

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Abstract

Machine learning is changing the world and fuelling Industry 4.0. These statistical methods focused on identifying patterns in data to provide an intelligent response to specific requests. Although understanding data tends to require expert knowledge to supervise the decision-making process, some techniques need no supervision. These unsupervised techniques can work blindly but they are based on data similarity. One of the most popular areas in this field is clustering. Clustering groups data to guarantee that the clusters’ elements have a strong similarity while the clusters are distinct among them. This field started with the K-means algorithm, one of the most popular algorithms in machine learning with extensive applications. Currently, there are multiple strategies to deal with the clustering problem. This review introduces some of the classical algorithms, focusing significantly on algorithms based on evolutionary computation, and explains some current applications of clustering to large datasets.

Item Type: Article
Keywords (uncontrolled): Clustering K-means Expectation-Maximization Spectral Clustering Evolutionary Computation Online Clustering
Item ID: 34309
Notes on copyright: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Depositing User: Jisc Publications Router
Date Deposited: 06 Jan 2022 17:11
Last Modified: 06 Jan 2022 17:11
URI: https://eprints.mdx.ac.uk/id/eprint/34309

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