Self-Organizing Maps possess unique properties that remove redundancies in a high-dimensional input space and map that input space to a low dimensional output space, thereby showing non-linear relationships in the input data. This ability makes Self-Organizing Maps attractive for measuring inter-limb coordination patterns. The current study takes previously published data (Bartlett, Bussey, & F...

In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the...

Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires the development of stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In this paper we present the Growing Hierarchical Self-Organizing Map (GH-SOM), a neural network model based on the self-organizing map. The main f...

This paper presents a new mapping to construct the self-organizing map on the curved seamless surface. This mapping is developed for the planar triangle surface derived from the conformal selforganizing map [C.-Y. Liou, Y.-T. Kuo, Conformal self-organizing map for a genus-zero manifold, Visual Comput. 21(5) (2005) 340–353]. It shows how to construct a seamless surface for the genus-zero manifol...

In this paper a Recurrent Self-Organizing Map (RSOM) algorithm is proposed for temporal sequence processing. The RSOM algorithm is close in nature to the Kohonen's Self-Organizing Map, except that in the RSOM context of the temporal sequence is involved both in the best matching unit nding and in the adaptation of the weight vectors of the map via an introduced recursive di erence equation asso...

Self-Organizing Maps are a prominent tool for exploratory analysis and visualization of high-dimensional data. We propose a novel method for visualizing the cluster structure and coherent regions of the Self-Organizing Map that can be displayed as a vector field on top of the map lattice. Concepts of neighborhood and proximity on the map is exploited to obtain a representation where arrows poin...

Multiscale structures and algorithms that unify the treatment of local and global scene information are of particular importance in image segmentation. Vector quantization, owing to its versatility, has proved to be an effective means of image segmentation. Although vector quantization can be achieved using self-organizing maps with competitive learning, self-organizing maps in their original s...

The self-organizing map’s unsupervised clustering property, is known for classifying high dimensional data sets into clusters that have similar features. Using this property and arranging self-organizing maps into hierarchies, we demonstrate in this paper that legacy code can be potentially broken down into suggested classes using hierarchical self-organizing maps. This is in conjunction with i...

In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknes...