Parallel Coordinates Representation of multi-dimensional data Discovery Process xmdv Visualization Tool Ganesh K. Panchanathan Christa M. Chewar

Cartesian Vs. Parallel Coordinates Speed( Mhz) 1500 Price($) 1500 Speed(Mhz)Price($) Speed(Mhz) Price($)

473 Items, 16 Dimensions X1 Yield X2 Quality X3 - X12 Defects X13 - X16 Physical Parameters

The Discovery Process Identify and understand Objectives Combine atomic queries to form complex queries Isolate batches with high X1 and X2 Batches with low X3 do not have high yield and quality

Isolate batches with Zero Defects in 9 attributes Isolate batches with Zero Defects in 8 attributes Small amounts of X3 and X6 defects necessary for high yield and quality All 9 batches have poor yield, quality Process sensitive to changes in X6

Further Insights Higher Range of split in X15 Low Yield Inconsistent Quality Lower Range of split in X15 High Yield High Quality

Conclusion Small Ranges of defects X3 and X6 are necessary Lower range of physical parameter X15 To get high yield and quality :

XMDV Tool

Grading Data

Pros Simplicity in Representation ( x D  2 D) Scalability ( any N) Visual cues from items having similar properties Uniform treatment of all variables Finds relationships between variables Combine atomic queries to form complex query Cons Difficulty with large data sets