comp3425 Data Mining

COMP3425 Data Mining S1 2022

Assignment 2

This assignment specification may be updated to reflect clarifications and modifications after it is first issued.

It is strongly suggested that you start working on the assignment right away. You can submit as many times as you like. Only the most recent submission at the due date will be assessed.

In this assignment, you are required to submit a single report in the form of a PDF file. You may also attach supporting information (appendices) as one or more identified sections at the end of the same PDF file. Appendices will not be marked but may be treated as supporting information to your report. Please use a cover sheet at the front that identifies you as author of the work using your Unumber and name and identifies this as your submission for COMP3425 Assignment 2. The cover sheet and appendices do not contribute to the page limit.

You are expected to write in a style appropriate to a professional report. You may refer to http://www.anu.edu.au/students/learningdevelopment/writing-assessment/report-writing for some stylistic advice. You are expected to use the question and sub-question numbering in this assignment to identify the relevant answers in your report.

No particular layout is specified, but you should use no smaller than 11 point typeface and stay within the maximum specified page count. Page margins, heading sizes, paragraph breaks and so forth are not specified but a professional style must be maintained. Text beyond the page limit will be treated as non-existent.

This is a single-person assignment and should be completed on your own. Make certain you carefully reference all the material that you use, although the nature of this assignment suggests few references will be needed. It is unacceptable to cut and paste another author's work and pass it off as your own. Anyone found doing this, from whatever source, will get a mark of zero for the assignment and, in addition, CECS procedures for plagiarism will apply.

No particular referencing style is required. However, you are expected to reference conventionally, conveniently, and consistently. References are not included in the page limit. Due to the context in which this assignment is placed, you may refer to the course notes or course software where appropriate (e.g. “For this experiment Rattle was used”), without formal reference to original sources, unless you copy text or images which always requires a formal reference to the source.

An assessment rubric is provided. The rubric will be used to mark your assignment. You are advised to use it to supplement your understanding of what is expected for the assignment and to direct your effort towards the most rewarding parts of the work.

Your submission will be treated confidentially. It will be available to ANU staff involved in the course for marking. It may be shared, de-identified, as an exemplar for other students.

Task

You are to complete the following exercises. For simplicity, the exercises are expressed using the assumption that you are using Rattle, however you are free to use R directly or any other data mining platform you choose that can deliver the required functions. You should describe the methods used in terms of the language of data mining, not in the terms of commands you typed or buttons you selected. You are expected, in your own words, to interpret selected tool output in the context of the learning task. Write just what is needed to explain the results you see.

1.       Platform

Briefly describe the platform for your experiments in terms of memory, CPU, operating system, and software that you use for the exercises. If your platform is not consistent throughout, you must describe it for each exercise. This is to ensure your results are reproducible.

2.       Data

(a)       In your own words, briefly describe the purpose and means of data collection.

(b)       Look at the pairwise correlation amongst the numeric variables using Pearson product-moment correlation. Qualitatively describe the pairwise correlations amongst each of the variables p_age_group_sdc, B1_a, B1_b, B1_c, B1_d, and B7. Explain what you see in terms of the meaning of the data.

3.       Association mining: What factors affect satisfaction with the country’s future?

A1 of the survey asks respondents how they feel about the direction of Australia. Your task is to use association

mining to find out which factors might be indicative of a person’s response to A1.

(a)       Generate association rules, adjusting min_support and min_confidence parameters as you need.

What parameters do you use? Bearing in mind we are looking for insight into what factors affect A1,

find 3 interesting rules, and explain both objectively and subjectively why they are interesting.

(b)       Comment on whether, in general, association mining could be a useful technique on this data.

4.       Study a very simple classification task

Aim to build a model to classify Opinionated. Use Opinionated as the target class and set every other variable (except srcid) as Input (independent). Using sensible defaults for model parameters is fine for this exercise where we aim to compare methods rather than optimise them.

(a)       This should be a very easy task for a learner. Why? Hint: Think how Opinionated is defined.

(b)       Train each of a Linear, Decision tree, SVM and Neural Net classifier, so you have 4 classifiers. Hint: Because the dataset is large, begin with a small training set, 20%, and where run-time speeds are acceptable, move up to a 70% training set. Evaluate each of these 4 classifiers, using a confusion matrix and interpreting the results in the context of the learning task.

(c)       Inspect the models themselves where that is possible to assist in your evaluation and to explain the performance results. Which learner(s) performed best and why?

5.       Predict a Numeric Variable

A3 of the survey asks respondents to rate their satisfaction on feeling about life in general. You are to train a regression tree or a neural net to predict A3, you may use any other variables but A1 as input.

(a)       Explain which you chose of a regression tree or neural net and justify your choice.

(b)       Train your chosen model and tune by setting controllable parameters to achieve a reasonable performance. Explain what parameters you varied and how, and the values you chose finally.

(c)       Assess the performance of your best result using the subjective and objective evaluation appropriate for the method you chose, and justify why you settled with that result.

6.       More Complex Classification

A2 of the survey asks respondents which political party they would vote for if an election were held now. Your task is to classify a person according to whether they are an undecided voter or not. An undecided voter is one who answered “Don’t know” to A2. Hint: The variable undecided_voter has transformed the values of A2 to a binary variable with values TRUE or FALSE, so you can use undecided_voter as your target. Hint: Be sure to ignore variable A2 when undecided_voter is your target. Hint: Initially, use a small training set, 20%, and where run-time speeds are acceptable, experiment with a larger training set.

(a)       Explain how you will partition the available dataset to train and validate classification models in

(b)       to (d) below.

(b)       Train a Decision Tree Classifier. You will need to adjust default parameters to obtain optimal performance. State what parameters you varied and (briefly) their effect on your results.

Evaluate your optimal classifier using the error matrix, ROC, and any quality information specific to the classifier method.

(c)       Train an SVM Classifier. Then proceed as for (b) Decision Tree above, using your SVM classifier instead.

(d)       Train a Neural Net classifier. Then proceed as for (b) Decision Tree above, using your Neural Net classifier instead.

7.       Clustering

(a) Restore the dataset to its original distributed form, removing any new variables you may have constructed above. For clustering, use the 3 raw variables, A1, p_age_group_sdc and p_education_sdc, plus 2 variables of your choice from B6_f, B6_g, and V1 (to total 5 variables). Ignore all the other variables.

Rescale the variables to fall in the range 0-1 prior to clustering. Use the full dataset for clustering (i.e. do not partition).

Experiment with clustering using the k-means algorithm by building cluster models for each of k= 2,

𝑛

5, √

2

(the latter is a recommended default for dataset of size n) clusters. Choose your preferred k

and its cluster model for k-means to answer the following.

(a)       Justify your choice of k as your preferred (Hint: have look at parts b-d below for each cluster model).

(b)       Calculate the sum of the within-cluster-sum-of-squares for your chosen model. The within- cluster-sum-of-squares is the sum of the squares of the Euclidean distance of each object from its cluster mean. Discuss why this is interesting.

(c)       Look at the cluster centres for each variable. Using this information, discuss qualitatively how each cluster differs from the others.

(d)       Use a scatterplot to plot (a sample of) the objects projected on to each combination of 2 variables with objects mapped to each cluster by colour (Hint: The Data button on Rattle’s Cluster tab can do this). Describe what you can see as the major influences on clustering. Include the image in your answer.

8.       Qualitative Summary of Findings (Hint: approx 1/2 page)

Comparatively evaluate the techniques you have used and their suitability or not for mining this data. This should be a qualitative opinion that draws on what you have found already doing the exercises above. For example, what can you say about training and classification speeds, the size or other aspects of the training data, or the predictive power of the models built? Finally, what else would you propose to investigate as a follow-up to your work presented here?

Assessment Rubric COMP3425 Data Mining

This rubric will be used to mark your assignment. You are advised to use it to supplement your understanding of what is expected for the assignment and to direct your effort towards the most rewarding parts of the work. Your assignment will be marked out of 100, and marks will be scaled back to contribute to the defined weighting for assessment of the course.