Neural Network World: Optimized Spiral Spherical Som (Oss-Som)

Abstract

Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

A Map of the European Insurance Sector – Are There any Borders

“…The results, which we obtained with the estimation of the OSS-SOM model, had to be transformed into a planar projection in order to allow for an interpretation of the data. We applied a special procedure for detecting similarities among neighbouring neurons that takes into consideration the deformations of the sphere (Jagric and Zunko, 2013). The obtained projection is shown in Figures 2 and 3, where the first figure is shown as a three dimensional topology, and the next figure is shown as a two dimensional topology.…”

“…We used the optimized spiral spherical SOM (OSS-SOM), a method introduced by Jagric (2013). This method overcomes a common problem in the application of self-organizing maps, namely the border effect (Jagric and Zunko, 2013). Additionally, this method was applied in the financial research area, for the dynamic analysis of banking sector integration (Jagric, Bojnec and Jagric, 2015), where it was stated that this methodology is particularly suitable for high dimensional data input spaces (big data).…”

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

“…Finally, other AI methods can also be tested to check if further classification results can be improved. By applying OSS-SOM [45], the impact of border effects on normal SOM can be studied. Additionally, besides unsupervised methods, also supervised methods can be applied.…”