Abstract
With the rapid growth in size and number of available databases, it is necessary to explore and develop new methods for analysing the huge amounts of data. Mining information and interesting knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Analyzing the huge amount (usually tera-bytes) of data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics, however, a very difficult task. Visual Exploration and Visual Data Mining techniques apply human visual perception to the exploration of large data sets and have proven to be of high value in exploratory data analysis. Presenting data in an interactive, graphical form often opens new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual exploration techniques and present new results obtained from applying PixelBarCharts in sales analysis and internet usage management.
Portions of this article have previously appeared in [11,12,15].
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Keim, D.A., Panse, C., Schneidewind, J., Sips, M., Hao, M.C., Dayal, U. (2003). Pushing the Limit in Visual Data Exploration: Techniques and Applications. In: Günter, A., Kruse, R., Neumann, B. (eds) KI 2003: Advances in Artificial Intelligence. KI 2003. Lecture Notes in Computer Science(), vol 2821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39451-8_4
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