Clustering analysis

Research output: Chapter in Book/Report/Conference proceedingChapter

22 Citations (Scopus)

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 languageEnglish
Title of host publicationMachine Learning
Subtitle of host publicationMethods and Applications to Brain Disorders
PublisherElsevier
Pages227-247
Number of pages21
ISBN (Electronic)9780128157398
ISBN (Print)9780128157398
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Brain disorders
  • Clustering analysis
  • DBSCAN
  • Disorder subtypes
  • Gaussian mixtures
  • K-means
  • Machine learning
  • Neurocogbitive profiles
  • fMRI

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