Abstract
The traditional centroid-based classifiers cannot be directly applied to categorical data classification due to the undefined concept of centroid for a categorical class, and the lack of an effective distance measure for categorical objects. In this paper, two centroid-based classifiers are proposed for categorical data classification. We propose a new formulation for the centroid of categorical classes to address the first problem, while two weighted distance measures are defined for the second problem. The experimental results conducted on real-world data sets show the effectiveness of the proposed methods.
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Chen, L., Guo, G. (2014). Centroid-Based Classification of Categorical Data. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_50
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DOI: https://doi.org/10.1007/978-3-319-08010-9_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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