By Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)
Self-organizing maps (SOMs) have been built via Teuvo Kohonen within the early eighties. on the grounds that then greater than 10,000 works were according to SOMs. SOMs are unsupervised neural networks priceless for clustering and visualization reasons. Many SOM functions were constructed in engineering and technology, and different fields.
This booklet comprises refereed papers awarded on the 9th Workshop on Self-Organizing Maps (WSOM 2012) held on the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop introduced jointly researchers and practitioners within the box of self-organizing platforms. one of the e-book chapters there are very good examples of using SOMs in agriculture, computing device technology, facts visualization, well-being platforms, economics, engineering, social sciences, textual content and snapshot research, and time sequence research. different chapters current the newest theoretical paintings on SOMs in addition to studying Vector Quantization (LVQ) equipment.
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Additional resources for Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings
Fig. 10. The result obtained when the map of Fig. 9 is stretched into the one dimension. The result is shown starting from the top panel for the 1st layer, then the 2nd layer and 3rd layer, respectively. In the top and bottom panels, the 4 corners are represented. The center position can’t be shown in one dimension. However, the center position of the 2nd layer can be shown in one dimension (middle panel, white circled). side. 20. 23. Due to the larger difference between the y coordinates than the x coordinates, the projected positions of the centers in Figs 9(c) and (d) can be understood.
SOM_PAK is free Classification of Chain-Link and Other Data with Spherical SOM 39 software of the regular SOM, developed at the Helsinki institute of technology in Finland . The result of the Torus-SOM is shown in Fig. 6. Let's pay attention to the Cyan mark (red circled) on the U-matrix in the central left of the figure. The mark in Fig. 6(a) was moved about 10 steps to the right in Fig. 6(b), as the map is continuous in either side. Similarly, in Fig. 6(c), it is moved up by about 5 steps compared with Fig.
Gray circles paired with dashed lines, and black circles paired with solid lines, show the weight vector positions and neighboring neuron connections upon initialization and after training, respectively. Unsupervised Weight-Based Cluster Labeling for SOMs 47 Neuron kj wkj1 wkj2 z1 : z11 z12 z2 : z21 z22 .. . zPT : zPT 1 zPT 2 ... z1I z2I .. . . zPT I (a) x wkjI ⎫ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭ x xx x x x DT (b) Fig. 1. The basic structure and operation of a SOM: (a) shows the SOM’s architecture; (b) shows the local eﬀect of map training in a hypothetical two-dimensional case Crosses denote the input space positions of training data vectors.