EMPIRICAL EVALUATION OF CLUSTERING ALGORITHMS

Unsupervised data classification can be considered one of the most important initial steps in the process of data mining. Numerous algorithms have been developed and are being used in this context in a variety of application domains. Albeit, only little evidence is available as to which algorithms should be used in which context, and which techniques offer promising results when being combined for a given task. In this paper we present an empirical evaluation of some prominent unsupervised data classification techniques with respect to their usability and the interpretability of their result representation.

• Publication year

  2000

• Venue

  Journal of information and organizational sciences

• Publication date

  2000-12-14

• Fields of study

  Computer Science

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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