Abstract
Semi-supervised clustering takes advantage of some labeled data called seeds to bring a great benefit to the clustering of unlabeled data. This paper presents a novel semi-supervised clustering method based on one-class support vector machine, which applies a novel decision rule to assigning the class label to one data point. To investigate the effectiveness of our approach, experiments are done on one artificial data set and two real datasets. Experimental results show that the proposed method can improve the clustering performance significantly compared to other semi-supervised clustering algorithms when using a very small amount of seeds.
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Gu, L. (2012). Applying a Novel Decision Rule to the Semi-supervised Clustering Method Based on One-Class SVM. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_15
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DOI: https://doi.org/10.1007/978-3-642-31346-2_15
Publisher Name: Springer, Berlin, Heidelberg
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