The Fictitious Super Happy Terrific Classifier Algorithm - IT Assignment Help

Assignment Task:

1. Weka recently added the fictitious Super Happy Terrific Classifier (SHTC) algorithm to its suite of available classifiers and you would like to use it in your analysis. Upon reading the SHTC documentation, you realize that it only accepts discrete attributes as input. However, many of the attributes in your data set are continuous. Can you still use the SHTC algorithm in your analysis? If yes, explain how. If no, explain why not. 

2. You have decided to use J48 as a classifier in Weka for your data set. After your analysis, you have found that the accuracy of J48 for your data set is greater than that of zero, but less than the accuracy of OneR. Should you continue to use J48 as a classifier for your data set? Why or why not? 

3. You have performed an unsupervised k-means clustering on a data set with two attributes and the results indicate a k of 2. Later, you determine the class values for each data instance (there are four class values) and a supervised clustering results in a k of 4. Provide a possible explanation for why the two clustering methods disagree on a k value and a draw a sketch of the two clusterings to go along with your explanation. 
 

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