1. Star Coordinates : A Multi-dimensional Visualization Technique with Uniform Treatment of Dimensions Rohit Kelapure & David Tussedorf

2. Multi-dimensional Data Treats each dimension uniformly
Follows the Info-Viz. Mantra :
Overview First
Zoom and Filter
Details on Demand

3. Star Co-ordinates : Curvilinear Co-ordinate System
Name
Co-ordinate Axes on a Circle with equal Angles .
Model-Year
MPG
Data-Points Scaled to Length
of axis.
Minimum Mapping : Origin
Maximum mapping : Other end of Axis
Acceleration
Cylinders
Weight
Displacement
Horsepower

4. Calculation of data point location for an 8-dimensional dataset
Name- 1
Star
Cartesian
Model-Year-8
MPG - 2
d j3
d j2
d j4
ON (x,y)
O(x,y) and sequence of N Two
dimensional Vectors.
An = < a1, a2, a3……. an >
d j1
d j5
Accl.-7
Cylinders -3 p
d j8
d j6
Data-element Dj
Point Pj
d j7
Weight -6
Displacement- 4
Horsepower – 5
Pj : Sum of all unit Vectors Ui = ( uxi , uyi )on each co-ordinate multiplied by the value of that data element for that co-ordinate

5. Hierarchially Clustered Datasets
Transformations :
Scaling
Rotations
Queries
Applications :
Hierarchially Clustered Datasets
Multifactor Analysis

6. Comparison Parallel Star Dimension inc. Space needed inc.
Space needed is constant.
Data- elements 
Lines showing exact Data Values
Data-elements 
Simpler & Space
Efficient points.