Software for replicating the results with the SKC and the KPC-A methods

Training and testing of clustering models with the KPC-A* and with the SKC** methods * Kernel clustering with approximate pseudo-centres (KPC-A) ** Semi-supervised kernel clustering with sample-to-cluster weights (SKC) The main purpose of this software is to replicate the experiments done in the publications listed below. The second purpose of this software is to allow others to re-use this software under the MIT license (see LICENSE). In case of re-use I kindly ask to cite the references below, where appropriate. Warning: The source code may consist of dead code, unused code (including unused parameters), wrongly documented code, or simply not working code parts. In short, it is (mainly) written for replicating the experiments and has not been (much) cleaned-up afterwards. No guarantees whatsoever. Currently supported baseline kernel methods: Kernel K-means (KKM), kernel fuzzy C-means (KFCM), relational neural gas (RNG). Supported kernel functions: Linear kernel, (normalized) polynomial kernel, Gaussian kernel. Evaluated data sets (all available from UCI): Gas, Pen, Cardiotocography, Activity, MiniBooNE. The software were used for the following articles: [1] Faußer, S. and Schwenker, F. (2012). "Clustering large datasets with kernel methods". In: Proceedings 21st International Conference on Pattern Recognition. (Tsukuba, Japan). ICPR ’12. IEEE Computer Society, pp. 501–504. [2] Faußer, S. and Schwenker, F. (2012). "Semi-Supervised Kernel Clustering with Sample-to-cluster Weights". In: Proceedings 1st IAPR TC3 Conference on Partially Supervised Learning. (Ulm, Germany). PSL’11. Springer, pp. 72–81. doi: 10.1007/978-3-642-28258-4_8. [3] Faußer, S. and Schwenker, F. (2014) "Semi-supervised Clustering of Large Data Sets with Kernel Methods". In: Pattern Recognition Letters 37, pp. 78–84. doi: 10.1016/j.patrec.2013.01.007. [4] Faußer, S. (2015). "Large state spaces and large data: Utilizing neural network ensembles in reinforcement learning and kernel methods for clustering". Doctoral thesis. URN: urn:nbn:de:bsz:289-vts-96149. URL: http://vts.uni-ulm.de/doc.asp?id=9614. In [1], the KPC-A method was introduced and in [2,3], the SKC method were proposed. Note that this software can be used to replicate the results of [4] only. Due to a lost seed, however, you won't get the very same results as in [4]. Still, with the seed set in this software, the results are in many cases identical to [4] or very close to them