Unsupervised Learning Algorithms in Big Data: An Overview

DOI

10.2991/978-2-494069-89-3_107How to use a DOI?

Keywords

Unsupervised Machine Learning; Big Data; Clustering algorithms; Dimensionality reduction algorithms; Applications

Abstract

With the progression of technology, there are more ways to produce complex and spiral data without signs. For the development of Artificial intelligence, machine learning is generated to help humans with human training or without. In this paper, based on the characteristics and properties of unsupervised algorithms, first, we are going to identify and classify methods of unsupervised dig data analysis into clustering and dimensionality reduction, and then systematically conclude the clustering algorithms (K-means, Hierarchical clustering, GMM, and DBSCAN) and dimensionality reduction algorithm (PCA, LLE, and MDS). Then, we will discuss some of those applications. Eventually, we will conclude and imagine the future development of big data analysis.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mohan Zhang
PY  - 2022
DA  - 2022/12/30
TI  - Unsupervised Learning Algorithms in Big Data: An Overview
BT  - Proceedings of the 2022 5th International Conference on Humanities Education and Social Sciences (ICHESS 2022)
PB  - Atlantis Press
SP  - 910
EP  - 931
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-89-3_107
DO  - 10.2991/978-2-494069-89-3_107
ID  - Zhang2022
ER  -