Motivation. A Botanically Inspired High- Dimensional Visualization with Multivariate Glyphs. Related Work

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1 A Botanically Inspired High- Dimensional Visualization with Multivariate Glyphs Eleanor Chlan & Penny Rheingans Motivation Small amounts of data are easy to represent Aggregation simplifies cognitive management New problem in understanding clusters Still need to understand data & relationships Need to compare data across hierarchy Need to handle high attribute data Need to discern variability and extent of data 1 2 Related Work Hierarchically oriented strategies Cone trees Treemaps[Shneiderman, 1992] Bubble Trees[Boardman, 2000] Cone trees[robertson,mackinlay,card, 1991] 1 Related Work [Kleiberg et. al. 2001] 1 Used by permission - Xerox PARC 3 4 1

2 The Data Set The Botanical Tree Model The The following examples are based on a census data set from the UC Irvine ML repository 30,000+ records 485 clusters Hierarchical clustering obtained through repetitive application of K-means 10 attributes: age, gender, marital status, etc. The Tree View The 5 Leaves The Tree View Builds tree using stochastic, parametric, Lsystem[Prusinkiewicz, et. al, 1990] Provides context Gives overview of dataset, facilitates navigation 6 Marital Status Dark Red (Married) to Green (Never Married) Probability to earn high salary: Dark red (very low probability) to Green Number of sub-clusters controls branching Relative cluster size controls diameter (high probability) 7 Hours worked per week Dark red (very few) to Green (very high) 8 2

3 Alternate Clustering Similarity Factors Leaf coloring is Marital Status Dark Red (Married) to Green (Never Married) Cluto [Karypis, et. al. 1999] 9 10 Based on the Stasko & Zhang Sunburst[2000] Shows the subtree starting at any node Successive rings (going outwards) reflect lower levels of the hierarchy (closer to leaves) Age Arc subtended by a node reflects its relative size All children of a node together subtend same degree of arc as the parent Leaf dividing lines omitted if degree of arc very small Color is average value of attribute Gender Education Marital Status Hours Worked Prob. High Salary

4 Simulates Shows wood cross-section one cluster All immediate subclusters as rings Reflects up to 5 attributes at one time Relative Leaf Occupation sizes reflects cluster sizes clusters have fixed ring sizes Hours worked Beech Cross Section Elliptical shape reflects quality High quality clusters - shorter minor axis - more oblate. Ring color reflects an attribute Bark reflects an attribute Max value --> thickness Min value --> height of troughs above wood Standard deviation --> width of bumps Average of attribute for entire data set --> color Photo by Thomas Siccama, Hubbard Brook Ecosystem Research Study, used with permission

5 Subcluster rings amended with Radial lines Dots Displays additional information Age - Ring and Bark color Age - Ring and Bark color Gender -Dots Gender -Dots Probability to earn high salary - Lines Probability to earn high salary - Lines 17 Summary and Conclusions Tree Thanks to The US Department of Defense The US National Science Foundation for their support High level overview Organizational context Simplifies assimilation of data View -medium level of detail Highlights strong clustering Branch George Karypis for making the Cluto code available And the authors who generously allowed use of their images View - high level of detail Shows extent and variability Direct comparison across children 18 Acknowledgements Metaphor - low level of detail Cluster