Comparative Evaluation of Four Methods for Exploratory Data Analysis

Research activities in many areas are associated with the use of various types of data. This data may contain important information, but this information is hidden in the data. In order to extract relevant information, this data must be subjected to appropriate processing and analysis. Data analysis allows to clarify the data structure and, if necessary, transform the data into a form that allows to extract the required information. In recent years powerful data analysis techniques have been developed. Typically, these methods are computationally complex and require the use of suitable software to implement. This paper summarizes and analyses four widely used methods of exploratory data analysis: principal component analysis, singular value decomposition, factor analysis, and linear discriminant analysis.