The present study devises two techniques for visualizing biological sequence data clusterings. The Sequence Data Density Display (SDDD) and Sequence Likelihood Projection (SLP) visualizations represent the input symbolical sequences in a lower-dimensional space in such a way that the clusters and relations of data elements are preserved as faithfully as possible. The resulting unified framework incorporates directly raw symbolical sequence data (without necessitating any preprocessing stage), and moreover, operates on a pure unsupervised basis under complete absence of prior information and domain knowledge.
Set of events from West Bohemian 2008 seismic swarm with known source mechanisms is processed. The events or their slips respectively are clustered into two groups: (i) principal events with slip laying in the main fault plane and (ii) complementary events deviating from that plane. From those slips we constructed image of slip distribution (a new way of data/slip presentation) and from slip distribution and variations we hypothesized about foci zone properties. Namely, we propose that western block is more rigid and compact; the eastern block appears to be constituted from several sub blocks which can interact with each other during the swarm course. Our hypothesis is supported by similar image constructed from relative rupture velocities, which we consider as independent data. The proposed structural model agrees with the existence of the different observed types of source mechanisms. and Kolář Petr, Boušková Alena.
This paper addresses the problem of clustering in large sets discussed in the context of financial time series. The goal is to divide stock market trading rules into several classes so that all the trading rules within the same class lead to similar trading decisions in the same stock market conditions. It is achieved using Kohonen self-organizing maps and the K-means algorithm. Several validity indices are used to validate and assess the clustering. Experiments were carried out on 350 stock market trading rules observed over a period of 1300 time instants.
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM.
In this article we present a novel method for mobile phone positioning using a vector space model, suffix trees and an information retrieval approach. The algorithm is based on a database of previous measurements which are used as an index which looks for the nearest neighbor toward the query measurement. The accuracy of the algorithm is, in most cases, good enough to accomplish the E9-1-1 standards requirements on tested data. In addition, we are trying to look at the clusters of patterns that we have created from measured data and we have reflected them to the map. We use Self-Organizing Maps for these purposes.
On the occasion of 80th birthday of an excellent Slovak philosopher Pavel Cmorej, some characteristic features of his work are presented. Cmorej is shown as a solid thinker who always took care of precisely expressing his thoughts. One of his remarkable works is a collection of his philosophical dialogues (published in 2007) where Cmorej’s analysis of various philosophical problems demonstrates his ability to develop analytic philosophy so that (the desirable) English translations of his works would certainly surprise his contemporary world analytic philosophers. and Pavel Materna
The article primarily describes the original results of Czech research into interactions between ants and plants in Central Europe. The role of myrmecophily is illustrated by long-term case studies (mountain pastures, industrial deposits) at the ecosystem level. Facilitation and acceleration of spontaneous vegetation succession by the ants’ activity with their nest construction and seed dispersal can be used as a tool in assisted ecological restoration. and Pavel Kovář.