2002/01/01 by Daniel A. Keim · 1,639 citations
Computer Science · #Computer science #Creative visualization #Data Management and Algorithms #Data Visualization and Analytics #Data mining #Data science #Data visualization #Information retrieval #Information visualization #Interactive visual analysis #Process (computing) #Video Analysis and Summarization #Visual analytics #Visualization
paper · open access · doi:10.1109/2945.981847
published in IEEE Transactions on Visualization and Computer Graphics 8(1), 1-8 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2002/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/27
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.