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2017 | OriginalPaper | Chapter

7. Unsupervised Learning

Authors : Laura Igual, Santi Seguí

Published in: Introduction to Data Science

Publisher: Springer International Publishing

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Abstract

In this chapter, we address the problem of analyzing a set of inputs/data without labels with the goal of finding “interesting patterns” or structures in the data. This type of problem is sometimes called a knowledge discovery problem. Compared to other machine learning problems such as supervised learning, this is a much more open problem, since in general there is no well-defined metric to use and neither there is any specific kind of patterns that we wish to look for. Within unsupervised machine learning, the most common type of problems is the clustering problem; though other problems such as novelty detection, dimensionality reduction and outlier detection are also part of this area. So here we will discuss different clustering methods, compare their advantages and disadvantages, and discuss measures for evaluating their quality. The chapter finishes with a case study using a real data set that analyzes the expenditure of different countries on education.

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Footnotes
2
The intracluster distance of sample i is obtained by the distance of the sample to the nearest sample from the same class, and the nearest-cluster distance is given by the distance to the closest sample from the cluster nearest to the cluster of sample i.
 
Literature
1.
go back to reference Press, WH; Teukolsky, SA; Vetterling, W.T.; Flannery, B.P. (2007). “Section 16.1. Gaussian Mixture Models and k-Means Clustering”. Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8. Press, WH; Teukolsky, SA; Vetterling, W.T.; Flannery, B.P. (2007). “Section 16.1. Gaussian Mixture Models and k-Means Clustering”. Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8.
2.
go back to reference Meilǎ, M.; Shi, J. (2001); “Learning Segmentation by Random Walks”, Neural Information Processing Systems 13 (NIPS 2000), 2001, pp. 873–879. Meilǎ, M.; Shi, J. (2001); “Learning Segmentation by Random Walks”, Neural Information Processing Systems 13 (NIPS 2000), 2001, pp. 873–879.
3.
go back to reference Székely, G.J.; Rizzo, M.L. (2005). “Hierarchical clustering via Joint Between-Within Distances: Extending Ward’s Minimum Variance Method”, Journal of Classification 22, 151–183. Székely, G.J.; Rizzo, M.L. (2005). “Hierarchical clustering via Joint Between-Within Distances: Extending Ward’s Minimum Variance Method”, Journal of Classification 22, 151–183.
Metadata
Title
Unsupervised Learning
Authors
Laura Igual
Santi Seguí
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-50017-1_7

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