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The k-Nearest-Neighbours(kNN) is a simple and effective method for data classification. One of the major drawbacks of kNN is its low efficiency in its testing phase due to the lack of an explicit classification model. Recently, kNN model-based classifiers have been proposed to improve the conventional kNN. However, their building incurs high computational costs. In this paper, we tackle the model...
Clustering categorical data faces two challenges, one is lacking of inherent similarity measure, and the other is that the clusters are prone to being embedded in different subspace. In this paper, we propose the first divisive hierarchical clustering algorithm for categorical data. The algorithm, which is based on multiple correspondence analysis (MCA), is systematic, efficient and effective. In...
The classification of categorical sequences is a fundamental process in many application fields. A key issue is to extract and make use of significant features hidden behind the chronological and structural dependencies found in these sequences. Almost all existing algorithms designed to perform this task are based on the matching of patterns in chronological order, but sequences often have similar...
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