1. COSC 6335 Fall 2014 Post Analysis Project1
Christoph F. Eick
Arko Barman

2. Post Analysis Project1 Disclaimer
The main purpose of these slides is not criticize groups but rather to learn how to do a better job when analyzing data and interpreting data mining results.
Most of you do not have much experience in these tasks
Learning without making errors is impossible; therefore, students can benefit from discussing errors of other students
Visualization
Use large, high resolution displays—some students used displays that did not reveal much because of too high density.
Be careful when you plot points of different attributes with different colors! 2 groups made plots with random color assignments to points
If you compare displays, put them next to each other!!
Use the same coordinate systems/scale in displays you compare

3. Post Analysis Project1 Part2
Interpretation
Scatterplot: the key question is if the attribute/pair of attributes can provide some evidence for the dominance of a particular class in a particular region in the attribute space; not if the attribute pair clearly separates the classes.
Vague interpretation of quantitative results; e.g. “Att1 seems to be more important that Att2” versus “the fact the regression coefficient of Att1 is 12 times as large as the regression coefficient of Att2 suggest that attribute Att1 has a much stronger impact on class membership”.
Overlooking patterns in displays; e.g. regions that are dominated by one class or only looking for pattern in E/W direction when there are also clear patterns in N/S direction.
Not giving summaries at all or giving very “quick” summaries

4. Some Displays
Group F

5. Regression Results
You needed to scale the data!!! Only one group provided weird results.
Class= * variance *skewness *curtosis *entropy
(Group G)
R-squared of the regression function is , which tell us the function fits the data well.
You needed to mention
Importance of attributes
Role of the sign of coefficients
How you normalized (there are more than one way)

6. Box Plots
Thanks to Group B!

7. Decision Trees
Mention how you divide the data into test and training sets
Bonus for cross-validation or trying out more than one way
<=10 nodes! One group had more than 20 nodes!!!
Analyze importance of attributes!
Group C

8. Conclusion (Q )
An excellent idea to remove entropy and plot the rest of the points! (Group A)

9. Post Analysis Project1 Part3
Statistical Summaries
If there are minor disagreement I took away 1 point
If the results do not make any sense, I took away a lot of points
Importance of Attributes
Variance likely to be the most important in classifying
Entropy does not have much impact
Curtosis and skewness somewhere in between

10. Post Analysis Project1 Part4
Linear Regression
If you do not scale data, interpretation of the observed coefficients is quite complicated
Lack of quantitative assessment of results
Star Plots
What is in your opinion the usefulness of this techniques?
I myself have difficulties making sense of those, but some of you do seem to like Star Plots much more...
Conclusion/Other Findings
Half of the groups of quite short conclusions and most summaries are somewhat vague; e.g. they do not write about
The importance/usefulness of the attributes
The usefulness of the employed techniques …