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Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Benefits of Visual Data Exploration
Classification of Visual Data Mining Techniques
Data Type to Be Visualized
One-Dimensional Data
Two-Dimensional Data
Multi-Dimensional Data
Specific Visual Data Mining Techniques
Conclusion
References

Visual Data-Mining Techniques

Profile image of Mihael AnkerstMihael AnkerstProfile image of Mike SipsMike Sips

2005, Visualization Handbook

https://doi.org/10.1016/B978-012387582-2/50045-9
visibility

…

description

27 pages

link

1 file

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 has become 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 that have been developed over the last two decades to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.

Key takeaways
sparkles

AI

  1. Visual data exploration integrates human insight into data analysis, enhancing hypothesis generation and discovery.
  2. An estimated 1 Exabyte of data is generated annually, necessitating advanced visualization techniques for effective exploration.
  3. The three-step visual exploration process includes overview, zoom and filter, and details-on-demand for efficient analysis.
  4. Classification of visual data techniques involves data type, visualization technique, and interaction technique, all being orthogonal.
  5. Future work should integrate visualization with traditional data analysis methods to improve the mining process.
Figures (17)
Fig. 1.1. Classification of Information Visualization Techniques
Fig. 1.1. Classification of Information Visualization Techniques
Fig. 1.2. Skitter Graph Internet Map, CAIDA (Cooperative Association for Internet Data Analysis) ©2000 UC Regents. Courtesy University of California.
Fig. 1.2. Skitter Graph Internet Map, CAIDA (Cooperative Association for Internet Data Analysis) ©2000 UC Regents. Courtesy University of California.
Fig. 1.3. Parallel Coordinate Visualization ©IEEE
Fig. 1.3. Parallel Coordinate Visualization ©IEEE
Fig. 1.4. Dense Pixel Displays: Recursive Pattern Technique ©IEEE
Fig. 1.4. Dense Pixel Displays: Recursive Pattern Technique ©IEEE
Fig. 1.5. The iris data set, displayed using star glyphs positioned based on the first two principal components (from XmdvTool [82]);
Fig. 1.5. The iris data set, displayed using star glyphs positioned based on the first two principal components (from XmdvTool [82]);
Fig. 1.6. Dense Pixel Displays: Circle Segments Technique ©IEEE
Fig. 1.6. Dense Pixel Displays: Circle Segments Technique ©IEEE
Fig. 1.7. Dimensional Stacking visualization of drill hole mining data (used by permission of M. Ward, Worcester Polytechnic Institute ©IEEE
Fig. 1.7. Dimensional Stacking visualization of drill hole mining data (used by permission of M. Ward, Worcester Polytechnic Institute ©IEEE
Fig. 1.8. MineSets Association Rule Visualizer [41] ©SGI
Fig. 1.8. MineSets Association Rule Visualizer [41] ©SGI
Fig. 1.9. Association Rule Visualization [38] ©ACM
Fig. 1.9. Association Rule Visualization [38] ©ACM
Fig. 1.10. MineSets Decision Tree Visualizer [41] ©SGI
Fig. 1.10. MineSets Decision Tree Visualizer [41] ©SGI
Fig. 1.11. Visualization of a decision trees [8] for the segment training data from the Statlog benchmark having 19 attributes ©ACM
Fig. 1.11. Visualization of a decision trees [8] for the segment training data from the Statlog benchmark having 19 attributes ©ACM
Fig. 1.12. Visualization based on a projection into 3D space [86] ©ACM
Fig. 1.12. Visualization based on a projection into 3D space [86] ©ACM
Fig. 1.13. OPTICS Visual Clustering [7] ©ACM
Fig. 1.13. OPTICS Visual Clustering [7] ©ACM
Fig. 1.14. HD-Eye screen-shot [37] showing different visualizations of projections and the separator tree. Clockwise from the top: separator tree, iconic representation of 1D projections, 1D projection histogram, 1D color-based density plots, iconic representa- tion of multi dimensional projections and color-based 2D density plot. QIEEE
Fig. 1.14. HD-Eye screen-shot [37] showing different visualizations of projections and the separator tree. Clockwise from the top: separator tree, iconic representation of 1D projections, 1D projection histogram, 1D color-based density plots, iconic representa- tion of multi dimensional projections and color-based 2D density plot. QIEEE
Fig. 1.15. Table Lens(used by permission of R. Rao, Xerox PARC ©ACM)
Fig. 1.15. Table Lens(used by permission of R. Rao, Xerox PARC ©ACM)
Fig. 1.16. A scatterplot matrix with part of the display distorted using a fisheye lens
Fig. 1.16. A scatterplot matrix with part of the display distorted using a fisheye lens
Fig. 1.17. Linked brushing between two multivariate visualization techniques, from XmdvTool [82]. Highlighted data (in red) from one display is also highlighted in other displays.
Fig. 1.17. Linked brushing between two multivariate visualization techniques, from XmdvTool [82]. Highlighted data (in red) from one display is also highlighted in other displays.

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Jose F Rodrigues-Jr

International Workshop on Visual Data Mining in conjunction with ICDM 2003, 2003

The challenge in the Information Visualization (Infovis) field is two-fold: the exploration of raw data with intuitive visualization techniques and the discover of new techniques to enhance the visualization power of well-known infovis approaches, improving the synergy between the user and the mining tools. This work pursues the second goal, presenting the use of interactive automatic analysis combined with visual presentation. To demonstrate such ideas, we present three approaches aiming to improve multivariate visualizations. The first approach, named Frequency Plot, combines frequencies of data occurrences with interactive filtering to identify clusters and trends in subsets of the database. The second approach, called Relevance Plot, corresponds to assign different shades of color to visual elements according to their relevance to a user’s specified set of data properties. The third approach makes use of basic statistical analysis presented in a visual format, to assist the analyst in discovering useful information. The three approaches were implemented in a tool enabled with refined versions of four well-known existent visualization techniques, and the results show an improvement in the usability of visualization techniques employed.

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