By Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel (auth.), Jorma Laaksonen, Timo Honkela (eds.)
This publication 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 rigorously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on plenaries; monetary and societal purposes; conception and method; purposes of knowledge mining and research; language processing and record research; and visualization and photo processing.
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Extra info for Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011, Espoo, Finland, June 13-15, 2011. Proceedings
Keywords: SOM, Network Representation, Eﬃciency, Health Care Providers. e. it should be able to achieve the speciﬁed outcomes in a way to maximise access, outputs and outcomes within the available resources. In the real world, however, this does not happen. Just to make an example, looking at the situation of Italy, health care expenditure plays a crucial impact into the ﬁnancial resources of the country; nevertheless our health care system is lesser eﬃcient than others, and it is not easy to explain why.
2 we saw the mapping of the attributive pronouns. The pronouns used as subjects or objects have histograms similar to those of the nouns, conﬁrming that it is the role of the words as sentence constituents rather than their linguistic class that is reﬂected in the contextual SOMs. The verbs, in general, are clustered into a round area in the middle of the SOM. An exceptional subset of verbs is formed by those 1035 verbs used as the core of a noun phrase. Independent of their frequency, they are mapped compactly into the lower right corner of the SOM, indicating that these verbs occur so tightly with the other words in the noun phrases that the latter words determine the location of the cluster on the SOM.
2), not the choice of the BMU (Eq. 1). Thus, the main purpose of the model is to visualize the evolution of financial indicators to assist the detection of vulnerabilities or threats to financial stability. The model is presented in detail in Sarlin  and a model on the same data set is evaluated in terms of out-of-sample accuracy in Sarlin and Marghescu . Moreover, a stand-alone FCM clustering has been applied on a close to similar data set in . 1, using the two principle components for initializing the reference vectors and the batch updating algorithm.