# Produce a hierarchical clustering model for iris data

- Use the following learning schemes to analyze the zoo data (in zoo.arff):

OneR

- weka.classifiers.OneR

Decision table - weka.classifiers.DecisionTable -R

C4.5 - weka.classifiers.j48.J48

K-means - weka.clusterers.SimpleKMeans

Try using reduced error pruning for the C4.5. Did it change the produced model? Why?

For K-means, for the first run, set k=10. Adjust as needed. What was the final number of k? Why?

- Use the following learning schemes to analyze the breast tumor data.

Linear regression

- weka.classifiers.LinearRegression

M5′ - weka.classifiers.M5′

Regression Tree - weka.classifiers.M5′

K-means clustering - weka.clusterers.SimpleKMeans

A) How many leaves did the Model tree produce? Regression Tree? What happens if you change the pruning factor?

How many clusters did you choose for the K-means method? Was that a good choice? Did you try a different value for k?

B) Now perform the same analysis on the bodyfat.arff data set.

- 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 models?
- Produce a hierarchical clustering (COBWEB) model for iris data. How many clusters did it produce? Why? Does it make sense? What did you expect?