Common clustering algorithms

I. Lee; J. Yang

doi:10.1016/B978-044452701-1.00064-8

Common clustering algorithms

I. Lee
, J. Yang

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Chapter

66 Citations (Scopus)

Abstract

This chapter surveys common clustering algorithms widely used in the data mining community in light of chemometrics. It starts with taxonomy of clustering algorithms, and discusses two common clustering approaches - partitioning clustering and hierarchical clustering - in detail. Several variants of these clustering methods are presented and their strengths and weaknesses are addressed. This chapter continues to overview hybrid clustering approaches combining partitioning clustering and hierarchical clustering, and concludes with a quick overview on constrained clustering.

Original language	English
Title of host publication	Comprehensive Chemometrics: Chemical and Biochemical Data Analysis
Editors	Steven D. Brown, Roma Tauler, Beata Walczak
Place of Publication	U.K.
Publisher	Elsevier
Pages	577-618
Number of pages	42
ISBN (Electronic)	9780444527011
ISBN (Print)	9780444527028
DOIs	https://doi.org/10.1016/B978-044452701-1.00064-8
Publication status	Published - 2009

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10.1016/B978-044452701-1.00064-8

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title = "Common clustering algorithms",

abstract = "This chapter surveys common clustering algorithms widely used in the data mining community in light of chemometrics. It starts with taxonomy of clustering algorithms, and discusses two common clustering approaches - partitioning clustering and hierarchical clustering - in detail. Several variants of these clustering methods are presented and their strengths and weaknesses are addressed. This chapter continues to overview hybrid clustering approaches combining partitioning clustering and hierarchical clustering, and concludes with a quick overview on constrained clustering.",

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AB - This chapter surveys common clustering algorithms widely used in the data mining community in light of chemometrics. It starts with taxonomy of clustering algorithms, and discusses two common clustering approaches - partitioning clustering and hierarchical clustering - in detail. Several variants of these clustering methods are presented and their strengths and weaknesses are addressed. This chapter continues to overview hybrid clustering approaches combining partitioning clustering and hierarchical clustering, and concludes with a quick overview on constrained clustering.

UR - http://handle.uws.edu.au:8081/1959.7/537446

U2 - 10.1016/B978-044452701-1.00064-8

DO - 10.1016/B978-044452701-1.00064-8

M3 - Chapter

SN - 9780444527028

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EP - 618

BT - Comprehensive Chemometrics: Chemical and Biochemical Data Analysis

A2 - Brown, Steven D.

A2 - Tauler, Roma

A2 - Walczak, Beata

PB - Elsevier

CY - U.K.

ER -

Common clustering algorithms

Abstract

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