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Experiment with Apriori and investigate the effect of the various parameters described before. Prepare a brief oral presentation on the main findings of your investigation.

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Exercise 4: Mining Association Rule with WEKA Explorer - Vote

Now consider a real-world dataset, vote.arff, which gives the votes of 435 U.S. congressmen on 16 key issues gathered in the mid-1980s, and also includes their party affiliation as a binary attribute. Association-rule mining can also be applied to this data to seek interesting associations.

Load data at Preprocess tab. Click the Open file button to bring up a standard dialog through which you can select a file. Choose the vote.arff file. To see the original dataset, click the Edit button, a viewer window opens with dataset loaded. This is a purely nominal dataset with some missing values (corresponding to abstentions).

Task 1. Run Apriori on this data with default settings. Comment on the rules that are

generated. Several of them are quite similar. How are their support and confidence values related?

Task 2. It is interesting to see that none of the rules in the default output involve Class = republican. Why do you think that is?

Exercise 5: Let's run Apriori on another real-world dataset.

Load data at Preprocess tab. Click the Open file button to bring up a standard dialog through which you can select a file. Choose the supermarket.arff file. To see the original dataset, click the Edit button, a viewer window opens with dataset loaded.

To do market basket analysis in Weka, each transaction is coded as an instance of which the attributes represent the items in the store. Each attribute has only one value: If a particular transaction does not contain it (i.e., the customer did not buy that item), this is coded as a missing value.

Task 1. Experiment with Apriori and investigate the effect of the various parameters described before. Prepare a brief oral presentation on the main findings of your investigation.

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