Abstract
Algorithms that extract information from data are required to provide correct information. However, data mining algorithms have an additional requirement. The information they extract must not only be correct, but also useful. The usefulness metric was developed to meet these needs. Although it has been shown to work on classification algorithms, the usefulness metric’s success lies in its ability to be applied to other data mining algorithms. This paper will show two different methods of applying the usefulness metric to a clustering algorithm in order to obtain more useful clusters.
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Clair, C.S. (2003). Finding the Most Useful Clusters: Clustering and the Usefulness Metric. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_55
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DOI: https://doi.org/10.1007/978-3-642-18991-3_55
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