2.21 - Unsupervised Data Mining: Introduction

D. Coomans; C. Smyth; I. Lee; T. Hancock; J. Yang

doi:10.1016/B978-0-444-64165-6.01021-1

2.21 - Unsupervised Data Mining: Introduction

D. Coomans, C. Smyth, I. Lee, T. Hancock, J. Yang

School of Computer, Data & Mathematical Sciences

James Cook University Queensland

Research output: Chapter in Book / Conference Paper › Chapter › peer-review

Abstract

This chapter focuses on cluster analysis in the context of unsupervised data mining. Various facets of cluster analysis, including proximities, are discussed in detail. Techniques of determining the natural number of clusters are described. Finally, techniques of assessing cluster accuracy and reproducibility are detailed. Techniques mentioned in this chapter are expanded upon in the following chapters.

Original language	English
Title of host publication	Comprehensive Chemometrics
Subtitle of host publication	Chemical and Biochemical Data Analysis, Second Edition: Four Volume Set
Publisher	Elsevier
Pages	465-477
Number of pages	13
Volume	2
ISBN (Electronic)	9780444641656
DOIs	https://doi.org/10.1016/B978-0-444-64165-6.01021-1
Publication status	Published - 1 Jan 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V. All rights reserved

Keywords

cluster analysis
cluster validity
data mining
proximities

Access to Document

10.1016/B978-0-444-64165-6.01021-1

Cite this

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2.21 - Unsupervised Data Mining: Introduction

Abstract

Bibliographical note

Keywords

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