1. Produce a hierarchical clustering (COBWEB) model for iris data. How many clusters did it produce? Why? Does it make sense? What did you expect?
Change
the acuity and cutoff parameters in order to produce a model similar to
the one obtained in the book. Use the classes to cluster evaluation –
what does that tell you?
2. Use the EM clustering method on either the basketball
or the cloud data set. How many clusters did the algorithm decide to
make? If you change from “Use Training set “ to “Percentage evaluation
split – 66% train and 33% test” – how does the evaluation change?
3. Use a k-means clustering technique to analyze the iris data set. What did you set the k value
to be? Try several different values. What was the random seed value?
Experiment with different random seed values. How did changing of these
values influence the produced model?
4. Choose one of
the following three files: soybean.arff, autoprice.arff, hungarian,
zoo.arff or zoo2_x.arff and use any two schemas of your choice to build
and compare the models. Which one of the models would you keep? Why?
Each question 250 words; Total 1000 -1200 words;
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