Abstract
Clustering analysis is a type of unsupervised learning which aims to find the most natural way of grouping a dataset. This is achieved by organizing a set of observations based on a similarity criterion, such that observations in the same group are more alike than observations in different groups. In this chapter we use K-means, the most popular clustering algorithm, to illustrate the main concepts, advantages, and limitations of clustering analysis. We then present alternative clustering algorithms including Gaussian mixture model and density-based spatial clustering of applications with noise. Finally, we illustrate some applications of K-means to the investigation of brain disorders and conclude with a series of recommendations.
Original language | English |
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Title of host publication | Machine Learning |
Subtitle of host publication | Methods and Applications to Brain Disorders |
Publisher | Elsevier |
Pages | 227-247 |
Number of pages | 21 |
ISBN (Electronic) | 9780128157398 |
ISBN (Print) | 9780128157398 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Keywords
- Brain disorders
- Clustering analysis
- DBSCAN
- Disorder subtypes
- Gaussian mixtures
- K-means
- Machine learning
- Neurocogbitive profiles
- fMRI