Cluster Analysis: Overview

S. Landau; I. Chis Ster

doi:10.1016/B978-0-08-044894-7.01315-4

Cluster Analysis: Overview

S. Landau, I. Chis Ster

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

5 Citations (Scopus)

Abstract

This article provides an overview of methods used to cluster data, that is, to discover and allocate objects to unknown subgroups. We review cluster analysis techniques for hierarchical, optimization, and model-based clustering. To derive at such techniques we first introduce the concept of proximity and then proceed to describe commonly used techniques for creating dendrograms, such as linkage methods and Wards method, and for searching for globally optimal partitions such as the popular k-means algorithm. Special attention is given to the issues of determining the number of clusters and checking cluster validity.

Original language	English
Title of host publication	International Encyclopedia of Education, Third Edition
Publisher	Elsevier
Pages	72-83
Number of pages	12
ISBN (Electronic)	9780080448947
DOIs	https://doi.org/10.1016/B978-0-08-044894-7.01315-4
Publication status	Published - 1 Jan 2009

Keywords

Agglomerative hierarchical clustering
Cluster number
Dendrogram
k-Means algorithm
Model-based clustering
Optimization clustering
Proximities
Validity check

Access to Document

10.1016/B978-0-08-044894-7.01315-4

Cite this

@inbook{a2349669c881426d85ce847c1a7e0eaf,

title = "Cluster Analysis: Overview",

abstract = "This article provides an overview of methods used to cluster data, that is, to discover and allocate objects to unknown subgroups. We review cluster analysis techniques for hierarchical, optimization, and model-based clustering. To derive at such techniques we first introduce the concept of proximity and then proceed to describe commonly used techniques for creating dendrograms, such as linkage methods and Wards method, and for searching for globally optimal partitions such as the popular k-means algorithm. Special attention is given to the issues of determining the number of clusters and checking cluster validity.",

keywords = "Agglomerative hierarchical clustering, Cluster number, Dendrogram, k-Means algorithm, Model-based clustering, Optimization clustering, Proximities, Validity check",

author = "S. Landau and {Chis Ster}, I.",

year = "2009",

month = jan,

day = "1",

doi = "10.1016/B978-0-08-044894-7.01315-4",

language = "English",

pages = "72--83",

booktitle = "International Encyclopedia of Education, Third Edition",

publisher = "Elsevier",

}

TY - CHAP

T1 - Cluster Analysis

T2 - Overview

AU - Landau, S.

AU - Chis Ster, I.

PY - 2009/1/1

Y1 - 2009/1/1

N2 - This article provides an overview of methods used to cluster data, that is, to discover and allocate objects to unknown subgroups. We review cluster analysis techniques for hierarchical, optimization, and model-based clustering. To derive at such techniques we first introduce the concept of proximity and then proceed to describe commonly used techniques for creating dendrograms, such as linkage methods and Wards method, and for searching for globally optimal partitions such as the popular k-means algorithm. Special attention is given to the issues of determining the number of clusters and checking cluster validity.

AB - This article provides an overview of methods used to cluster data, that is, to discover and allocate objects to unknown subgroups. We review cluster analysis techniques for hierarchical, optimization, and model-based clustering. To derive at such techniques we first introduce the concept of proximity and then proceed to describe commonly used techniques for creating dendrograms, such as linkage methods and Wards method, and for searching for globally optimal partitions such as the popular k-means algorithm. Special attention is given to the issues of determining the number of clusters and checking cluster validity.

KW - Agglomerative hierarchical clustering

KW - Cluster number

KW - Dendrogram

KW - k-Means algorithm

KW - Model-based clustering

KW - Optimization clustering

KW - Proximities

KW - Validity check

UR - http://www.scopus.com/inward/record.url?scp=85147070006&partnerID=8YFLogxK

U2 - 10.1016/B978-0-08-044894-7.01315-4

DO - 10.1016/B978-0-08-044894-7.01315-4

M3 - Chapter

AN - SCOPUS:85147070006

SP - 72

EP - 83

BT - International Encyclopedia of Education, Third Edition

PB - Elsevier

ER -

Cluster Analysis: Overview

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this