Abstract
We propose a new method for clustering categorical data. Clustering algorithms need to be designed specifically for categorical data because it has a different nature from numerical data. Here our focus is on the partition paradigm of algorithms. One existing approach is to transform categorical data into binary data and then use k-means. However it’s computationally inefficient. Another approach is k-modes, which extends k-means by replacing means with modes. In our work, we show that the center-based objective function of k-modes can not produce accurate clustering results. Instead, we propose an objective function that is generalized from the k-means objective, but not based on centers. We show that it’s more effective than the center-based objective and demonstrate it with real-life datasets. We also find that by using a particular algorithm called transfer algorithm, the proposed objective function can be efficiently solved. Thus our method is both efficient and effective.
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Xiang, Z., Ji, L. (2013). The Use of Transfer Algorithm for Clustering Categorical Data. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_6
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DOI: https://doi.org/10.1007/978-3-642-53917-6_6
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