The paper presents results of GUHA analysis of proteomic data. The data are related to an oncological study on breast cancer and are given by 2D electrophoresis gels carrying expression intensity of proteins in cancer cells. The gels have been classified by a physician according to the clinical course of the tumor disease. A research task is aimed on search for significant relations between protein spot intensities and respective clinical presentation. The task was solved by the GUHA method of data mining.
The research reported in the paper is a part of a large project aiming
at designing an automatic device for the micro-sleep events detection. In the paper we are interested in the classification of EEG spectrograms with respect to the level of attention (mentation, relaxation, micro-sleep) of a monitored person (a proband). Data mining techniques are ušed for developing a classification model. Namely, GUHA method is employed for this purpose. It is a method of exploratory data analysis established on logical and statistical bases that has been continuously developed for last 40 years in the Czech Republic.
We show that prediction of travel time on a 28-km long highway section based on on-line travel time measurements with video is practicable by a data mining method. We introduce a new prediction model, a result of the GUHA style data mining analysis and tlie Total Fuzzy Similarity method. Comparing the results with the existing Traficon model, oiir model improves the travel time class prediction. The results obtained by our method are comparable to the MLP neural network model, too.