Abstract
Similarity measure is very important in data mining techniques such as clustering, nearest-neighbor classification, outlier detection and so on [1][4]. There are many similarity measures have been proposed. For numeric data, there are many Minkowski distance-based similarity measures. However, the similarity measures for categorical data have been studied for a long time, it also has many issues. The main issue is to understand relationship between categorical attribute values. For categorical data, the similarity measure is not clear as well as numeric data. In this paper, we propose a new approach to understand relationship between categorical data. This approach is based on artificial neural network to extract significant features for computing distance between two categorical data objects.
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© 2011 Springer-Verlag Berlin Heidelberg
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Jin, C.H., Li, X., Lee, Y.K., Pok, G., Ryu, K.H. (2011). A New Approach for Calculating Similarity of Categorical Data. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Communications in Computer and Information Science, vol 206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24106-2_74
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DOI: https://doi.org/10.1007/978-3-642-24106-2_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24105-5
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