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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A New Approach for Calculating Similarity of Categorical Data
verfasst von : Cheng Hao Jin, Xun Li, Yang Koo Lee, Gouchol Pok, Keun Ho Ryu
Erschienen in: Convergence and Hybrid Information Technology
Verlag: Springer Berlin Heidelberg
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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.