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Volatility of selected separators/classifiers wrt. data sets from field of particle physics
Jiřina, Marcel ; Hakl, František
We study the volatility, i.e. influence of random changes in data sets to overall separation/classification behavior of separators/classifiers. This is motivated by the fact, that simulated data and true data from ATLAS experiment may differ, and a question arises what if separators or cuts are optimized for simulated data, and then used for true data from the experiment. This behavior was studied using simulated data modified by artificial distortions of known size. We found that even slight change in data sets causes a little worse result than supposed but, surprisingly, even relatively large distortions give then nearly the same results. Only truly great variations cause degradation of separation quality of separator/classifier as well as of the cuts method.
Fulltext: content.csg -  PDF
Plný tet: v1126-11 - PDF
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New Developments in Fuzzy Cluster Analysis
Řezanková, H. ; Húsek, Dušan
The paper deals with a special class of cluster analysis methods where a membership degree is calculated for each object and each cluster. These methods are investigated under the name fuzzy cluster analysis. We present some emerging topics in this area, such as relation fuzzy clustering, soft clusters ensembles, similarity of fuzzy clusters, visualization of clustering results, simultaneous clustering and feature discrimination, and techniques for cluster number determination. Some tasks are illustrated by clustering of binary variables.
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Použití Matlabu pro kvantitativní analýzu růstu kvasinek
Schier, Jan ; Kovář, Bohumil
Quantitative growth analysis is used in microbiology, genetics and medical re- search to determine the in uence of the tested substance on the growth of colonies of some organism (we focus on yeast colonies in this paper). Examples of the tested substance include antibiotics, mutagens or medical drugs. To evaluate the experiment, time series of images of the colonies inoculated on Petri dishes are taken by digital camera in a darkroom. Then, relative area and the number of the colonies in each image have to be determined for further statistical processing. In the current laboratory practice, these parameters have often been hand-counted in each image, which is an error-prone process of a limited precision. Also, given the time demands of this hand-counting, the number of snapshots that can reasonably be processed is rather limited. In this paper, we introduce a Matlab-based tool for semi-automated processing of the dish images.
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Klasifikátor založený na inverzních hodnotách indexů II. teorie a příloha
Jiřina, Marcel ; Jiřina jr., M.
A theory of a new method for the classification of data into classes is presented. The method is based on the sum of reciprocals of neighbors' indexes. We show that neighbors' indexes are in close relation to the approximate polynomial transform of the neighbors' distances. The sum of the reciprocals of indexes for all neighbors forms truncated harmonic series due to a finite number of its elements. For the neighbors of one class there is a sum of the selected elements of this truncated series. It is proved that the ratio of these sums gives just the probability that the point to be classified - the query point - is of that class.
Fulltext: content.csg - PDF
Plný tet: v1041-08 - PDF
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