Self-organizing maps
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Semi-supervised graph clustering: a kernel approach
Machine Learning
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Semi-supervised clustering of large data sets with kernel methods
Pattern Recognition Letters
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Collecting unlabelled data is often effortless while labelling them can be difficult. Either the amount of data is too large or samples cannot be assigned a specific class label with certainty. In semi-supervised clustering the aim is to set the cluster centres close to their label-matching samples and unlabelled samples. Kernel based clustering methods are known to improve the cluster results by clustering in feature space. In this paper we propose a semi-supervised kernel based clustering algorithm that minimizes convergently an error function with sample-to-cluster weights. These sample-to-cluster weights are set dependent on the class label, i.e. matching, not-matching or unlabelled. The algorithm is able to use many kernel based clustering methods although we suggest Kernel Fuzzy C-Means, Relational Neural Gas and Kernel K-Means. We evaluate empirically the performance of this algorithm on two real-life dataset, namely Steel Plates Faults and MiniBooNE.