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.
Journal
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Publication Name
Comprehensive Chemometrics: chemical and biochemical data analysis
Volume
2
ISBN/ISSN
978-0-444-52702-8
Edition
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Issue
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Pages Count
43
Location
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Publisher
Elsevier
Publisher Url
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Publisher Location
Oxford, UK
Publish Date
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Url
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Date
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EISSN
N/A
DOI
10.1016/B978-044452701-1.00064-8