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

  • Stefan Faußer
  • Friedhelm Schwenker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7081)


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.


Class Label Sample Label Normalize Mutual Information Cluster Assignment Cluster Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefan Faußer
    • 1
  • Friedhelm Schwenker
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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