This research focuses on big data visualization that is based on dimensionality reduction methods. We propose a multi-level method for data clustering and visualization. It divides the whole data mining process into separate steps and applies particular dimensionality reduction method considering to analyzed data volume and type. The methods are selected according to their speed and accuracy. Therefore, we present a comparison of the selected methods according to these two criteria. Three groups of datasets containing different kind of data are used for methods evaluation. The factors that influence speed or accuracy are determined. The rank of investigated methods based on research results is presented in this paper.

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Abstract. This research focus on prediction of anomalous situations in financial markets. It investigates if the indicator of investors' sentiment can be used to eliminate the factor of irrational behaviour and increase the profits. The overview of existing models for financial crisis forecasting is presented in this paper. Financial and sentiment-based indicators are overviewed and the general classification of both kind of indicators is suggested. The methodology of using investors' sentiment indicator together with different strategies are presented. The simulation of investment to different financial instruments was used in order to test the proposed method. The results show that sentiment based indicator can successfully prevent the investments from losses.