On Using Class-Labels in Evaluation of Clusterings

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Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
Although clustering has been studied for several decades, the fundamental problem of a valid evaluation has not yet been solved. The sound evaluation of clustering results in particular on real data is inherently difficult. In the literature, new clustering algorithms and their results are often externally evaluated with respect to an existing class labeling. These class-labels, however, may not be adequate for the structure of the data or the evaluated cluster model. Here, the authors survey the literature of different related research areas that have observed this problem.
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