Spatial sciences are confronted with increasing amounts of high-dimensional data. These data commonly exhibit spatial and temporal dimensions. To explore, extract, and generalize inherent patterns in large spatio-temporal data sets clustering algorithms are indispensable. These clustering algorithms must account for the distinct special properties of space and time in order to outline meaningfu...

Image segmentation is very important in computer based image interpretation and it involves the labeling of the image so that the labels correspond to real world objects. In this study, we utilise a novel approach to automatically segment out the ventricular system from a series of MR brain images and to recover the shape of hand outlines from a series of 2D training images. Automated landmark ...

Introduction The self-organization of sensotopic maps, in particular of visual maps, continues to be an area of great interest in computational neuroscience. In order to distinguish between the diierent map formation models and between the spe-ciic self-organization mechanisms they assume, their behavior with regard to an as large number of physiological, anatomical or theoretical constraints a...

In this paper a process for the detection of clusters in oceanographic data is described. The application to oceanographic data is relevant as it allows the improvement of the understanding of the phenomena occurring in the Portuguese coast. Additionally, the application also illustrates how the self-organizing maps maybe used to explore and explain clusters, especially emphasizing the relevanc...

In this paper, empirical results are presented which suggest that size and rate of decay of region size plays a much more significant role in the learning, and especially the development, of topographic feature maps. Using these results as a basis, a scheme for decaying region size during SOM training is proposed. The proposed technique provides near optimal training time. This scheme avoids th...

The supervised self-organizing map consists in associating output vectors to input vectors through a map, after self-organizing it on the basis of both input and desired output given altogether. This paper compares the use of Euclidian distance and Mahalanobis distance for this model. The distance comparison is made on a data classification application with either global approach or partitionin...

When a user visits a web site, important information concerning his/her preferences and behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site. From the set of possible that define the visitor’s behavior, two have been selected: the visi...

The use of self-organizing maps to analyze data often depends on finding effective methods to visualize the SOM’s structure. In this paper we propose a new way to perform that visualization using a variant of Andrews’ Curves. Also we show that the interaction between these two methods allows us to find sub-clusters within identified clusters.

In this work the classification of Force Expiratory volume in 1 second (FEV 1) in pulmonary function test is carried out using Spirometer and Self Organizing Feature Map Algorithm. Spirometry data are measure with flow volume spirometer from subject (N=100 including Noramal, and Abnormal) using standard data acquisition protocol. The acquire data are then used to classify FEV1. Self Organizing ...

Abstract This paper describes recent efforts to model the remarkable ability of humans to recognize speech and words. Different techniques for representing phonological similarity between words in the lexicon with self-organizing algorithms are discussed. Simulations using the Standard Kohonen algorithm are presented to illustrate some problems confronted with this technique in modeling similar...