The aim of this paper is to assess the influence of several indicators determining innovativeness of countries' economies by applying selected soft computing methods. Such methods enable us to identify correlations between indicators for period 2006-2010. The main attention in the paper is focused on selecting proper computer tools for solving this problem. As a tool supporting identification, ...

We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast (Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-w...

Kohonen self-organizing neural networks, also called self-organizing maps (SOMs), have been used successfully to recognize human phonemes and in this way to aid in human speech recognition. This paper describes how SOMS also can be used to associate specific information content with animal vocalizations. A SOM was used to identify acoustic units in Gunnison's prairie dog alarm calls that were v...

Semiotic interpretation of lexical cohesion is a major research challenge both in theoretical and applied linguistics. From the point of view of usagebased language description, individual lexical units can be roughly characterized by their collocation profiles, i.e., by collections of condensed usage patterns extracted from very large corpora. It is posited that related lexical units tend to s...

Self-Organizing Maps (SOMs) are often visualized by applying Ultsch’s Unified Distance Matrix (U-Matrix) and labeling the cells of the 2-D grid with training data observations. Although powerful and the de facto standard visualization for SOMs, this does not provide for two key pieces of information when considering real world data mining applications: (a) While the U-Matrix indicates the locat...

We humans are good at detecting visual patterns. Driving a car, scanning the headlines of newspapers or noticing that a single tile is out of alignment in your bathroom, during most of our daily activities we rely heavily upon the information we can extract from what our eyes can see. Our affinity for visual imagery can be used to effectively and efficiently convey information. This has been do...

A major disadvantage of feedforward neural networks is still the difficulty to gain insight into their internal functionality. This is much less the case for, e.g., nets that are trained unsupervised, such as Kohonen’s self-organizing feature maps (SOMs). These offer a direct view into the stored knowledge, as their internal knowledge is stored in the same format as the input data that was used...

An arrival time of an elastic wave is the important parameter to visualize locations failures and/or velocity distributions in field non-destructive testing (NDT). The detection conducted generally using automatic picking algorithms a measured time-history waveform. According algorithms, it expected that detected from low S/N signals has accuracy if are measurements. Thus, order accurately dete...

Today with the rapid development of information technology, it is becoming more and more important to be able to share traffic information between various traffic management administrations. The purpose of this paper is to discuss a data fusion algorithm based on Self Organizing Maps (SOMs) for Integrated Traffic Information System (ITIS), which is one of an essential part of Intelligent Transp...

The Self-Organizing Map (SOM) attracts attentions for clustering in these years. In our past study, we have proposed a method using simultaneously two kinds of SOMs whose features are different, namely, one self-organizes the area on which input data are concentrated, and the other self-organizes the whole of the input space. Further, we have applied this method to clustering of data including ...