ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel-based subtractive clustering method
Pattern Recognition Letters
A survey of kernel and spectral methods for clustering
Pattern Recognition
Active semi-supervised fuzzy clustering
Pattern Recognition
Soft clustering using weighted one-class support vector machines
Pattern Recognition
Survey of clustering algorithms
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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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.