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A Survey of Clustering Data Mining Techniques

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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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  • DOI: 10.1007/3-540-28349-8_2
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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

  • eBook Packages: Computer ScienceComputer Science (R0)