Advances in Self-Organizing Maps: 9th International - download pdf or read online

By Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)

ISBN-10: 3642352294

ISBN-13: 9783642352294

ISBN-10: 3642352308

ISBN-13: 9783642352300

Self-organizing maps (SOMs) have been built through Teuvo Kohonen within the early eighties. for the reason that then greater than 10,000 works were in keeping with SOMs. SOMs are unsupervised neural networks beneficial for clustering and visualization reasons. Many SOM functions were constructed in engineering and technological know-how, and different fields.

This booklet includes refereed papers offered 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 structures. one of the publication chapters there are first-class examples of using SOMs in agriculture, desktop technological know-how, info visualization, overall healthiness structures, economics, engineering, social sciences, textual content and photo research, and time sequence research. different chapters current the most recent theoretical paintings on SOMs in addition to studying Vector Quantization (LVQ) equipment.

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Additional info for Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings

Example text

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. 6(b). Fig. 6. (a) After Torus-SOM learning, (b) the map (a) is slightly moved to the right, and (c) moved upwards. (d) The result obtained with SOM_PAK learning. Finally, the result obtained after learning, using the SOM_PAK is shown in Fig. 6(d). In the case of the Torus-SOM of Figs. 6, the map is continuous on all sides.

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. 3 Analysis by Torus-SOM and Ordinary SOM Next, the result obtained with an ordinary SOM is shown in Fig. 11. SOM_PAK is used for the analysis. The cases of 2 layers and 3 layers are shown in Fig. 11. Here, the interval d equals 10. As evidenced by Fig. 11, the boundaries between the layers are clearly shown by the U-matrix. Next, the learning is repeated for the cases of 2 layers and 3 layers with the Torus-SOM.

Sm }, of m meta-level clusters of weight vectors, called emergent clusters. A cluster contains weight vectors with similar characteristics, which represent similar data examples. Many emergent weight vector discovery methods are available, including exploratory methods [26] and algorithms such as SOM-Ward [16] and k-means [15] clustering. The specifics of these techniques are not this paper’s focus. Step 2: Compute Significance Measures: Significance values are computed for each cluster’s attributes.

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Advances in Self-Organizing Maps: 9th International Workshop, WSOM 2012 Santiago, Chile, December 12-14, 2012 Proceedings by Barbara Hammer, Andrej Gisbrecht, Alexander Schulz (auth.), Pablo A. Estévez, José C. Príncipe, Pablo Zegers (eds.)


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