Abstract
All the machine learning techniques we have seen so far have one thing in common and that is availability of labelled training data. However, there are numerous cases, when such data is too expensive and unrealistic to get. In this chapter we are going to study the algorithms that can work without labelled training data and still be able to produce certain insights into the data or reduce the dimensionality of the data. All these algorithms are called as unsupervised algorithm and their application is called as unsupervised learning. Unsupervised learning marks an important pillar of modern machine learning.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Self Organizing Maps https://en.wikipedia.org/wiki/Self-organizing_map
Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification John Wiley and Sons, 2006.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Joshi, A.V. (2020). Unsupervised Learning. In: Machine Learning and Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-26622-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-26622-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26621-9
Online ISBN: 978-3-030-26622-6
eBook Packages: EngineeringEngineering (R0)