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 e-book constitutes the refereed complaints of the eighth foreign 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 a variety of submissions. The papers are equipped in topical sections on plenaries; monetary and societal functions; concept and method; functions of knowledge mining and research; language processing and record research; and visualization and picture processing.

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Finally, the organization of clusters provides information at technical level too, suggesting that wraparound grid topologies (either toroidal or cylindric) could reach more satisfying results. 5 Conclusion In this paper we discussed an application of Self Organizing Map (SOM) to assess the efficiency of health care providers. To do this, we examined by means of SOM the data of the balance sheet of 300 italian health care providers that receive public fundings. Since, to the best of our knowledge, this is a first–time application in the health care sector, we mainly addressed our efforts to the application of classic SOM algorithm.

138]. 42 P. Sarlin and T. Eklund The units of the map can further be divided into clusters of similar units. Instead of dividing the units into crisp clusters, we employ the FCM algorithm, developed by [13] and improved by [8], for assigning a degree of membership of each unit in each of the clusters. The FCM algorithm implements an objective function-based fuzzy clustering method. The objective function J μ is defined as the weighted sum of the Euclidean distances between each unit and each cluster center, where the weights are the degree of memberships of each unit in each cluster, and constrained by the requirement that the sum of memberships of each point equals 1: Jμ = M C ∑∑ u ik μ mi − c k 2 C , i =1 k =1 ∑ u ik = 1 , (3) k =1 where μ ∈ (1, ∞ ) is the fuzzy exponent, uik is the degree of membership of reference vector mi (where i=1,2,…,M) in the cluster center ck (where k=1,2,…,C, and 1

We have used it to retrieve more information about their efficiency, and to suggest some economic interpretations of the results. This paper is therefore organized as follows: Section 2 briefly describes the problem we focused on, and the data we have considered in the study; Section 3 provides a glimpse on the literature that deals on the alternatives to standard Euclidean metric, and then illustrates the changes carried on SOM algorithm to use it with norms derived from (1). Section 4 illustrates the details of our simulations, and discusses the results we have obtained.

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