Specific Usage of Visual Data Analysis Techniques

Abstract

Data mining processes are computer intensive and algorithm dependent processes. Visualization tools may be very useful in solving data mining problems. Today’s information flow demands the use of special algorithms for data analysis and data mining. The data are often automatically recorded by sensors and monitoring systems, cash and credit card paying machines etc.. For all items, many variables are recorded, resulting in data with a high dimensionality. The data are collected because people believe that it is a potential source of valuable information, providing new insights or a competitive advantage [4]. But, finding valuable information hidden in the data, however, is a difficult task. Information visualization tools and visual data analysis can help to deal with the flood of information. A great advantage of visual data exploration is the direct involvement of the user. Visual data mining integrates the human in the data analysis process. The human perceptual abilities help the analysis of today's large data sets [1]. Visual data mining is especially useful when little is known about the data and when the exploration goals are vague. Visual data exploration can be seen as a hypothesis generation process where the visualizations of the data allow setting new hypotheses. The main advantages of Visual data exploration (VDE) is that it can easily deal with highly non-homogeneous and noisy data, it is intuitive and requires no understanding of complex mathematical or statistical algorithms or parameters and can provide a qualitative overview of the data, allowing data phenomena to be isolated for further quantitative analysis.