Summary
Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.
Keywords
- Singular Value Decomposition
- Association Rule
- Numerical Attribute
- Categorical Attribute
- Graph Partitioning
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2006 Springer-Verlag Berlin Heidelberg
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Berkhin, P. (2006). A Survey of Clustering Data Mining Techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds) Grouping Multidimensional Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28349-8_2
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DOI: https://doi.org/10.1007/3-540-28349-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28348-5
Online ISBN: 978-3-540-28349-2
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