COSC2111/2110: Ethical issues in Data Mining - Parameters Error Fitting - IT Assignment Help

Assignment Task:

1 Overview 

This assignment is concerned with some kinds of tasks that occur in practical data mining situations. In this assignment you are asked to apply a number of algorithms to a number of data sets and write a report on your findings. Your assignment will be assessed on desmontrated understanding of concepts, algorithms, methodology, analysis of results and conclusions. Please make sure your answers are labelled correctly with the corresponding part and sub-question numbers, to make it easier for the marker to follow. 

2 Learning Outcomes 

This assessment relates to the following learning outcomes of the course. • CLO 1: Demonstrate advanced knowledge of data mining concepts and techniques. 

• CLO 2: Apply the techniques of clustering, classification, association finding, fea ture selection and visualisation on real world data. 

• CLO 4: Apply data mining software and toolkits in a range of applications. 

• CLO 5: Set up a data mining process for an application, including data preparation, modelling and evaluation. 

• CLO 6: Demonstrate knowledge of ethical considerations involved in data mining. 

3 Assignment Details 

3.1 Part 1: Classification (12 marks) 

This part of the assignment is concerned with the file: 

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/ 

/data/arff/UCI/hypothroid.arff.

The data was supplied by the Garavan Institute and J. Ross Quinlan, NSW, Australia. The main goal here is to achieve the highest classification accuracy with the lowest amount of overfitting. 

1. Run the following classifiers, with the default parameters, on this data: ZeroR, OneR, J48, IBK and construct a table of the training and cross-validation errors. You can get the training error by selecting “Use training set” as the test option. What do you conclude from these results? Provide your explanation. 

Run No Classifier Parameters Training Cross-valid Over 

Parameters Error Error Fitting 

1 ZeroR None 30.0% 30.0% None 

. . . . . 

2. Using the J48 classifier, can you find a combination of the C and M parameter values that minimizes the amount of overfitting? Include the results of your best five runs, including the parameter values, in your table of results. What is your conclusion? 

3. Reset J48 parameters to their default values. What is the effect of lowering the number of examples in the training set? Provide your explanation. Include your runs in your table of results. 

4. Using the IBk classifier, can you find the value of k that minimizes the amount of overfitting? Provide your explanation. Include your runs in your table of results. 

5. Try two other classifiers. Aside from ZeroR, which classifiers are best and worst in terms of predictive accuracy? Include 5 runs in your table of results. Provide your analysis on these results. 

6. Compare the accuracy of ZeroR, OneR and J48. What do you conclude? Give your explanation on these results. 

7. What golden nuggets did you find, if any? 

8. [OPTIONAL for COSC2110] Use an attribute selection algorithm to get a reduced attribute set. How does the accuracy on the reduced set compare with the accuracy on the full set? Provide your explanation. 

Report Length: Up to two pages, not including the table of runs. 

3.2 Part 2: Numeric Prediction (8 marks) 

This part of the assignment is concerned with the file: 

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/ 

data/arff/numeric/cpu.with.vendor.arff. 

The task is to predict the value of the “class” attribute. The main goal is to achieve the lowest mean absolute error with the lowest amount of overfitting. 

1. Run the following classifers, with default parameters, on this data: ZeroR, MP5, IBk and construct a table of the training and cross-validation errors. You may want to turn on “Output Predictions” to get a better sense of the magnitude of the error on each example. What do you conclude from these results? Give your explanation. 

2. Explore different parameter settings for M5P and IBk. Which values give the best performance in terms of predictive accuracy and overfitting? Include the results of the best five runs in your table of results. Provide your explanation on these results. 

3. Investigate two other classifiers for numeric prediction and their associated param eters. Include your best five runs in your table of results. Which classifier gives the best performance in terms of predictive accuracy and overfitting? Provide your explanation. 

4. What golden nuggets did you find, if any? 

Report Length Up to one page, not including the table of runs. 

3.3 Part 3: Clustering (10 marks) 

Clustering of the hypothroid data of part 1. For this part use only the attributes age, sex, TSH and TT4. The aim is to determine the number of clusters in the data and assess whether any of the clusters are meaningful. 

1. Run the K-means clustering algorithm on this data for the following values of K: 1,2,3,4,5,10,20. Analyse the resulting clusters. What do you conclude? Provide your reasoning. 

2. Choose a value of K and run the algorithm with different seeds. What is the effect of changing the seed? Provide your explanation. 

3. Run the EM algorithm on this data with the default parameters and describe the output and your analysis. 

4. The EM algorithm can be quite sensitive to whether the data is normalized or not. Use the Weka normalize filter 

(Preprocess --> Filter --> unsupervised --> normalize) 

to normalize the numeric attributes. What difference does this make to the clus tering runs? Provide your reasoning. 

5. The algorithm can be quite sensitive to the values of minLogLikelihoodImprove mentCV minStdDev and minLogLikelihoodImprovementIterating, Explore the effect of changing these values. What do you conclude? 

6. How many clusters do you think are in the data? Give an plain English language description of one of them. 

7. Compare the use of K-means and EM for these clustering tasks. Which do you think is best? Why? 

8. What golden nuggets did you find, if any? 

Report Length Up to one page. 

3.4 Part 4: Association Finding (5 marks) 

Association finding in the files supermarket1-small.arff and supermarket2-small.arff in the folder: 

/KDrive/SEH/SCSIT/Students/Courses/COSC2111/DataMining/data/arff. The main aim is to determine whether there are any significant associations in the data. 

These files contain the same details of shopping transactions represented in two different ways. You can use a text viewer to look at the files. 

1. What is the difference in representations? 

2. Load the file supermarket1-small.arff into Weka and run the Apriori algorithm on this data. You might need to restrict the number of attributes and/or the number of examples. What significant associations can you find? Provide your reasoning. 

3. Explore different possibilities of the metric type and associated parameters. What do you find? Provide your explanation. 

4. Load the file supermarket2-small.arff into Weka and run the Apriori algorithm on this data. What do you find? Provide your explanation. 

5. Explore different possibilities of the metric type and associated parameters. What do you find? Provide your explanation. 

6. Try the other associators. What are the differences to Apriori? 

7. What golden nuggets did you find, if any? 

8. [OPTIONAL for COSC2110] Can you find any meaningful associations in the hy pothroid data? 

Report Length Up to one page. 

3.5 Part 5: Ethical issues in Data Mining (5 marks) 

In this task, you will need to provide a 5-minute recorded video presentation (with no more than 5 presentation slides). The topic can be an ethical or legal issue involving data mining. Your talk should clearly articulate an ethical or legal issue in a real-world data mining application scenario, and your suggestions on how to handle the issue. You will need to record the presentation in WEBM format (using Studio in Canvas). Both the recorded video presentation and the presentation slides (PDF format) should be submitted through Canvas.

 

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