Download Advances in Self-Organizing Maps: 8th International by Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, PDF

By Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)

This booklet constitutes the refereed court cases of the eighth overseas Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised complete papers provided have been conscientiously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on plenaries; monetary and societal purposes; concept and technique; purposes of information mining and research; language processing and rfile research; and visualization and photograph processing.

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Notice that if the target word has a distance of less than five words from these specific symbols, the five-word contexts could not be formed. Nonetheless the original corpus was so large that the remaining amount of text (488,878 words) was still believed to produce statistically significant results. 18 T. Kohonen and H. Xing The size of the original lexicon used in this work was 48,191. The number of words actually used, restricting to pure contexts only, was 27,090. The patches of the SOM array were selected as hexagonal, and the size of the array was relatively small, 40 by 50, in order to save memory but still to be able to discern the cluster structures on it.

This enables sensible representation of the real world filled with uncertainty and imprecision. The model is not only expected to provide an adequate clustering, but also to enable easily interpretable visualizations of the evolution of cluster memberships over time. As the crispness of the data cannot be known a priori, the FCM clustering presents information on the overlapping of the clusters, be they crisp or fuzzy, while still always enabling comparisons between data points. We apply FCM clustering to two previously presented SOM models for financial time-series analysis: financial benchmarking of companies [9] and monitoring indicators of currency crises [10].

Generally speaking, the best in class companies are in clusters A and B, and poorest in clusters G and H. 44 P. Sarlin and T. 2 Fuzzy Clustering of the Financial Benchmarking Model The reference vectors from the financial benchmarking model were used as input for a second–level clustering. 0) and the c-value (between 3 and 9). 0 provided the best visual interpretability of the map, introducing a fuzziness degree large enough to show relationships between clusters, but not large enough to completely eliminate cluster borders.

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