43 Visual Data - Mining Techniques *

Never before in history have data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data-mining process. There are a large number of information visualization techniques that have been developed over the last few years to support the exploration of large datasets. In this chapter, we provide an overview of information visualization and visual data-mining techniques and illustrate them using a few examples. The progress made in hardware technology allows today’s computer systems to store very large amounts of data. Researchers from the University of Berkeley estimate that every year about 1 exabyte (1 million terabytes) of data is generated, of which a large portion is available in digital form. This means that in the next three years more data will be generated than in all of human history to date. The data is often automatically recorded via sensors and monitoring systems. Even simple transactions of everyday life, such as paying by credit card or using the telephone, are typically recorded by computers. Usually many parameters are recorded, resulting in data with high dimensionality. The data is collected because people believe that it is a potential source of valuable information, providing a competitive advantage (at some point). Finding the valuable information hidden in the data, however, is a difficult task. With today’s data-management systems, it is possible to view only small portions of the data. If the data is presented textually, the amount of data that can be displayed is in the range of some one hundred data items, but this is like a drop in the ocean when you are dealing with datasets containing millions of data items. Having no possibility to adequately explore the large amounts of data that have been collected because of their potential usefulness, the data becomes useless and the databases become data ‘dumps.’ Information visualization focuses on datasets lacking inherent 2D or 3D semantics and therefore also lacking a standard mapping of the abstract data onto the physical screen space. There are a number of well known techniques for visualizing such datasets, such as x-y plots, line plots, and histograms. These techniques are useful for data exploration but are limited to relatively small and low-dimensional datasets. In the last few years, a large number of novel information visualization techniques have been developed, allowing visualizations of multidimensional datasets without inherent 2D or 3D semantics. Nice overviews of the approaches can be found in a number of recent books [8,38,38,28]. The techniques can be classified based on three