comp3425 Data Mining

COMP3425 Data Mining S1 2022

Assignment 2


Maximum marks



25% of the total marks for the course


Maximum of 10 pages, excluding cover sheet, bibliography and



A4 margin, at least 11 point type size, use of typeface, margins

and headings consistent with a professional style.

Submission deadline

9:00am, Monday, 9 May

Submission mode

Electronic, via Wattle

Estimated time

15 hours

Penalty for lateness

100% after the deadline has passed

First posted:

29th March, 10 AM

Last modified:

29th March, 10 AM

Questions to:

Wattle Discussion Forum


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 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.


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.

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.


Review Criteria

Max Mark






1.       Platform &

2.       Data



1. Platform description complete (memory, CPU, operating system,




1. Platform description complete (memory, CPU, operating system, software).


1. Platform description complete (memory, CPU, operating system, software).


1. Platform description incomplete.


2a. Incomplete or faulty




2a. Demonstrates understanding of the purposes and process sufficient to frame report.

2a. Clear description of the

the data domajn.


2b. Partially clear and correct explanation in terms of data


2a. Attempt but unclear


2b. Partial description of variables or unclear

2b. Partial explanation in

data context


2b. Description unrelated to correlation of variables.

2b. Explanation unrelated to data source



2b. All correlations for

mentioned variables clearly explained in terms






of the data semantics, in

the correct directions and for correct or plausible domain reasons.














创建时间:2022-05-24 17:12