Semi-Supervised Kernel Clustering with Sample-to-Cluster Weights
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.
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