Lab Exercise Two Mining Association Rule with WEKA Explorer
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1 Lab Exercise Two Mining Association Rule with WEKA Explorer 1. Fire up WEKA to get the GUI Chooser panel. Select Explorer from the four choices on the right side. 2. To get a feel for how to apply Apriori, start by mining association rules from the weather.nominal.arff data set of Lab One. Note that Apriori algorithm expects data that is purely nominal: If present, numeric attributes must be discretized first. 3. Click Associate Tab on top of the window. 4. Left click the field of Associator, choose Show Property from the drop down list. The property window of Apriori opens:
2 5. Weka runs an Apriori-type algorithm to find association rules, but this algorithm is not exact the same one as we discussed in class. a. The min. support is not fixed. This algorithm starts with min. support as upperboundminsupport (default 1.0 = 100%), iteratively decrease it by delta (default 0.05 = 5%). Note that upperboundminsupport is decreased by delta before the basic Apriori algorithm is run for the first time. b. The algorithm stops when lowerboundminsupport (default 0.1 = 10%) is reached, or required number of rules numrules (default value 10) have been generated. c. Rules generated are ranked by metrictype (default Confidence). Only rules with score higher than minmetric (default 0.9 for Confidence) are considered and delivered as the output. d. If you choose to show the all frequent itemsets found, outputitemsets should be set as True. 6. Click Start button on the left of the window, the algorithm begins to run. The output is showing in the right window.
3 The algorithm starts with min.support as 100% and stops at 15% after running 17 times. 7. You could re-run Apriori algorithm by selecting different parameters, such as lowerboundminsupport, minmetric (min. confidence level), and different evaluation metric (confidence vs. lift), and so on. 8. You could save the result of one run in a separate file. Right click the run you want to save, choose Save result buffer.
4 You could save the content in the result window in a file named as weather.result. And later open it with Microsoft Word or other editor. 9. Let s work on the data set given in Homework no.2., the cosmetic data set. The binary format of the data set should be changed to the following format in Excel for Weka. And all attributes should be transformed from numeric value to nominal value before applying Apriori algorithm. The first attribute Trans. Should be removed as well. Trans Nail Polish (a) Brushes (b) Concealer ( c) Bronzer (d) Bag (e) Blush (f) Eyebrow Pencils (h) ? 1? 2 1? 1 1??? 3?? 1 1? ??? 5?? 1 1? 1? 6?? 1???? ? 1? 8 1 1? 1??? 9?? 1???? ?? 1 1? 11 1?? 1??? ???
5 10. When run Apriori on cosmetic.csv data set, please change the lower bound of min. support as 0.3. Check the frequent itemsets found by Weka. Are they the same as what you found by hands? 11. 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. Let s use association-rule mining can also be applied to this data to seek interesting associations. We are on Preprocess now. 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). Exercise 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? Exercise 2. It is interesting to see that none of the rules in the default output involve Class republican. Why do you think that is? 12. Let s run Apriori on another real-world dataset. We are on Preprocess now. 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. Exercise. Experiment with Apriori and investigate the effect of the various parameters described before. Write a brief report on the main findings of your investigation.
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