Clustering algorithms and validity measures

Clustering aims at discovering groups and identifying interesting distributions and patterns in data sets. Researchers have extensively studied clustering since it arises in many application domains in engineering and social sciences. In the last years the availability of huge transactional and expe...

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Bibliographic Details
Published in:Proceedings Thirteenth International Conference on Scientific and Statistical Database Management. SSDBM 2001 pp. 3 - 22
Main Authors: Halkidi, M., Batistakis, Y., Vazirgiannis, M.
Format: Conference Proceeding
Language:English
Published: IEEE 2001
Subjects:
Biomedical engineering
Clustering algorithms
Clustering methods
Data mining
Engineering in medicine and biology
Informatics
Partitioning algorithms
Pattern recognition
Unsupervised learning
ISBN:9780769512181, 0769512186
ISSN:1099-3371
Online Access:Get full text
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Summary:Clustering aims at discovering groups and identifying interesting distributions and patterns in data sets. Researchers have extensively studied clustering since it arises in many application domains in engineering and social sciences. In the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. The paper surveys clustering methods and approaches available in the literature in a comparative way. It also presents the basic concepts, principles and assumptions upon which the clustering algorithms are based. Another important issue is the validity of the clustering schemes resulting from applying algorithms. This is also related to the inherent features of the data set under concern. We review and compare clustering validity measures available in the literature. Furthermore, we illustrate the issues that are under-addressed by the recent algorithms and we address new research directions.
ISBN:9780769512181
0769512186
ISSN:1099-3371
DOI:10.1109/SSDM.2001.938534