Our Favorite Sentence “The display of multivariate datasets in parallel coordinates transforms the search for relations among the variables into a 2D pattern recognition problem”
Discovery Process Multivariate datasets Discover relevant relations among variables
An Example Production data of 473 batches of a VLSI chip Measurements of 16 parameters - X1,..,X16 Objective –Raise the yield X1 –Maintain high quality X2 Belief: Defects hindered yield and quality. Is it true?
The Full Dataset X1 is normal about its median X2 is bipolar
Example (contd..) Batches high in yield, X1 and quality, X2 Batches with low X3 values not included in selected subset
Example (contd..) Batches with zero defect in 9 out of 10 defect types All have poor yields and low quality
Example (contd..) Batches with zero defect in 8 out of 10 defect types Process is more sensitive to variations in X6 than other defects
Example (contd..) Isolate batch with the highest yield X3 and X6 are non-zero Defects of types X3 and X6 are essential for high yield and quality
Critique Strengths –Low representational complexity –Discovery process well explained –Use of parallel coordinates is very effective Weaknesses –Does not explain how axes permutation affects the discovery process –Requires considerable ingenuity –Display of relations not well explained –References not properly cited
Related Work InfoCrystal [Anslem Spoerri] –Visualizes all possible relationships among N concepts –Example: Get documents related to visual query languages for retrieving information concerning human factors
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