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Visualizing Association R ules in Groceries

Visualizing Association R ules in Groceries. Yuqing Yang CS548 Showcase Prof. Carolina Ruiz. References. [1] Hahsler M, Chelluboina S. Visualizing Association Rules: Introduction to the R-extension Package arulesViz [J]. R project module, 2011.

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Visualizing Association R ules in Groceries

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  1. Visualizing Association Rules in Groceries Yuqing Yang CS548 Showcase Prof. Carolina Ruiz

  2. References • [1] Hahsler M, Chelluboina S. Visualizing Association Rules: Introduction to the R-extension Package arulesViz[J]. R project module, 2011. • [2] Wong P C, Whitney P, Thomas J. Visualizing association rules for text mining[C]//Information Visualization, 1999.(Info Vis' 99) Proceedings. 1999 IEEE Symposium on. IEEE, 1999: 120-123, 152. • [3] Ertek, Gürdal, and AyhanDemiriz. "A framework for visualizing association mining results." Computer and Information Sciences–ISCIS 2006. Springer Berlin Heidelberg, 2006. 593-602. • [4] Hofmann, Heike, Arno PJM Siebes, and Adalbert FX Wilhelm. "Visualizing association rules with interactive mosaic plots." Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2000. • [5] Jeffrey Heer, Stuart K. Card, James Landay (2005). "Prefuse: a toolkit for interactive information visualization". In: ACM Human Factors in Computing Systems CHI 2005.

  3. 1. Scatter Plot • Axes: two interest measures. • Color (gray level) – a third measure. Figure 1: Scatter Plot[1]

  4. Two-key plot • Color – Order, the number of items contained in the rule. Figure 2: Scatter Plot[1]

  5. 2. Matrix-based Visualizations Figure 3: Matrix-based visualization of two measures with colored squares[1] Figure 4: Matrix-based visualization of two measures with colored squares (reordered)[1]

  6. Matrix-based Visualizations • Explore different antecedents which have a similar impact on the same consequent in terms of the measure used in the plot. • X-axes – consequent itemsets. • Y-axes – consequent itemsets. • is the selected interest measure for the i-th rule.

  7. Matrix-based Visualizations 3D Bars Figure 5: Matrix-based visualization with 3D bars[1]

  8. 3. Grouped Matrix-based Visualization • Columns -- antecedent groups • Rows – consequents • Color – aggregated interest measure • Size of ballo0n-- aggregated support Figure 6: Grouped matrix with k=?[1]

  9. 4. Graph-based Visualizations Figure 8: Itemsets as vertices[1] Figure 9: Rules as vertices[1]

  10. 5.Parallel Coordinates Plot Figure 10: Parallel coordinate plot (reordered)[1]

  11. Parallel Coordinates Plot • To visualize multidimensional data. • X-axis -- the positions in a rule, i.e., first item, second item, etc. • Head of arrow -- points to the consequent item. • The width of the arrows -- support • The intensity of the color -- confidence.

  12. Visualizing AR in Text Mining • A visualization of item associations with support > 0.4% and confidence > 50%. Figure 11: A visualization of item associations)[2]

  13. Infovis application Framework • Prefuse: a toolkit for interactive information visualization • Provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations[5] • E.g. racialgraphic • Ajax.org (Javascript) • AnyChart (Flash) • Axiis • Degrafa • ExtJs

  14. Inforvis Software and tools • Software • Panopticonhttp://www.panopticon.com/ • Circos (Perl) http://circos.ca/ • Balsamiq (hand-draw style) http://webdemo.balsamiq.com/ • Web infovis • Easel.ly (story telling) http://www.easel.ly/ • Piktocharthttp://piktochart.com/ • Visual.ly http://visual.ly/ • Infogr.am http://infogr.am/

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