Information visualization and visual data mining

TLDR

The explosion of data volumes has made exploration and analysis increasingly difficult, prompting the development of numerous information visualization techniques that directly involve users in the mining process. This paper proposes a classification of information visualization and visual data mining techniques based on data type, visualization method, and interaction/distortion technique. The classification framework groups techniques by the type of data they visualize, the visualization method employed, and the interaction or distortion mechanisms used. The authors illustrate the classification with several examples drawn from techniques and systems featured in this special section.

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.