keim - IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER...

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Information Visualization and Visual Data Mining Daniel A. Keim, Member , IEEE Computer Society Abstract —Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming 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 which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized , the visualization technique , and the interaction and distortion technique . We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section. Index Terms —Information visualization, visual data mining, visual data exploration, classification. æ 1I NTRODUCTION T HE 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 are 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 before. 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 multidimen- sional data with a high dimensionality. The data of all mentioned areas 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 them, however, is a difficult task. With today’s data management systems, it is only possible to view quite small portions of the data. If the data is presented textually, the amount of data which can be displayed is in the range of some 100 data items, but this is like a drop in the ocean when dealing with data sets containing millions of data items. Having no possibility of adequately exploring the large amounts of data which have been collected because of their potential usefulness, the data becomes useless and the databases become data “dumps.” 1.1 Benefits of Visual Data Exploration For data mining to be effective, it is important to include the human in the data exploration process and combine the flexibility, creativity, and general knowledge of the human with the enormous storage capacity and the computational power of today’s computers. Visual data exploration aims at integrating the human in the data exploration process,
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keim - IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER...

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