Abstract
Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, or mixed attributes. Accordingly, an iterative clustering algorithm is developed, whose efficacy is experimentally demonstrated on different benchmark data sets.
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Cheung, Ym., Jia, H. (2013). A Unified Metric for Categorical and Numerical Attributes in Data Clustering. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_12
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DOI: https://doi.org/10.1007/978-3-642-37456-2_12
Publisher Name: Springer, Berlin, Heidelberg
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