The toolkit can be downloaded from Attachments
PbSOMBox Version 1.0: This toolbox includes Matlab implementations of probabilistic selforganizing maps (PbSOM) learning algorithms, i.e., KohonenGaussian, SOCEM, SOEM, and SODAEM, which are presented in the paper: "Modelbased Clustering by Probabilistic SelfOrganizing Maps," IEEE Trans. on Neural Networks, 2009. This toolbox is released by ShihSian Cheng, who is the corresponding author of the paper.
Demostration
 Simuliations on 2D data (You need to click on the (GIF) figures to see the demo)
 Data set: The data set consists of 2000 points uniformly distributed in a unit square.
 Network structure: In the experiments, an 5 by 5 equally spaced square lattice in a unit square is used as the structure of the SOM network.
 Visualization of highdimentional data
 Data set: The Ecoli data set from UCI machine learning repository, which consists of 336 8dimensional feature vectors. It is comprised of eight classes, namely cp: C, im: I, pp: P, imU: U, om: O, omL: M, imL: L, and imS: S. The numbers of data samples are 143, 77, 52, 35, 20, 5, 2, and 2, respectively.
 Network structure: In the experiments, an 7 by 7 equally spaced square lattice in a unit square is used as the structure of the SOM network.
 Results: For each algorithm we see that the topological relationships among data clusters can be visualized on a twodimensional network (lattice).

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PbSOMBox_Version1.0.zip (523k) 鄭士賢, Aug 16, 2010, 1:04 AM
