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Journal ArticleDOI

Information visualization and visual data mining

Daniel A. Keim
- 01 Jan 2002 - 
- Vol. 8, Iss: 1, pp 1-8
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TLDR
This paper proposes 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.
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.

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Citations
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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

TL;DR: Providing an in-depth examination of core text mining and link detection algorithms and operations, this text examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches.
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Toward a Deeper Understanding of the Role of Interaction in Information Visualization

TL;DR: Seven general categories of interaction techniques widely used in Infovis are proposed, organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system.
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Big data

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A Brief Survey of Text Mining.

TL;DR: The main analysis tasks preprocessing, classification, clustering, information extraction and visualization are described and a number of successful applications of text mining are discussed.
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A Multi-Level Typology of Abstract Visualization Tasks

TL;DR: A multi-level typology of visualization tasks is contributed to address the gap between why and how a visualization task is performed, as well as what the task inputs and outputs are.
References
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Proceedings ArticleDOI

The eyes have it: a task by data type taxonomy for information visualizations

TL;DR: A task by data type taxonomy with seven data types and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) is offered.
Book

Information Visualization: Perception for Design

TL;DR: The art and science of why the authors see objects the way they do are explored, and the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility, and persuasiveness.
Book

Readings in Information Visualization: Using Vision to Think

TL;DR: In this paper, the authors present a method for using vision to think in higher-level visualisation, focusing on space, interaction, focus + context, text, and context.
Journal ArticleDOI

Generalized fisheye views

TL;DR: This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
Book

Visualizing Data

TL;DR: Using a downloadable programming environment developed by the author, Visualizing Data demonstrates methods for representing data accurately on the Web and elsewhere, complete with user interaction, animation, and more.