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COMP3425 Data Mining S1 2021

Assignment 2
Maximum marks 100
Weight 20% of the total marks for the course
Length Maximum of 10 pages, excluding cover sheet, bibliography and appendices.
Layout A4 margin, at least 11 point type size, use of typeface, margins and headings consistent with a professional style.
Submission deadline 9:00am, Monday, 10 May
Submission mode Electronic, via Wattle
Estimated time 15 hours
Penalty for lateness 100% after the deadline has passed
First posted: 26th March, 5:00 PM
Last modified: 22th April, 11:00 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
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.
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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, C3_a, C3_b, C3_c,C3_d,C3_e, and C3_f . 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.
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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
B6 of the survey asks respondents to rate their agreement on “There has been too much unnecessary
worry about the COVID-19 outbreak”. You are to train a regression tree or a neural net to predict B6,
you may use any other variables 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.
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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 C1_a to C1_i (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,
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?

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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
Exemplary Excellent Good

Acceptable Unsatisfactory
1. Platform &
2. Data

10 9-10
1.Platform description
complete (memory, CPU,
operating system,
software).

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

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.




7-8
1. Platform description
complete (memory, CPU,
operating system, software).

2a Clear description of the
the data domajn.



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










5-6
1. Platform description
complete (memory, CPU,
operating system, software).

2a Attempt but unclear


2b Partial description of
variables or unclear

2b Partial explanation in data
context
0-4
1. Platform description
incomplete.

2a Incomplete or faulty


2b Description unrelated to
correlation of variables.

2b Explanation unrelated to
data source

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Criteria Max Exemplary Excellent Good Acceptable Unsatisfactory
3. Association
mining
10 9-10
a. Answers demonstrate deep
understanding of association
mining, by the careful
selection of interesting and
differentiated rules and clear
rationale for interestingness.

b. Comment shows original
and insightful analysis of
association mining on the
problem.


7-8
a Support and confidence
clear
a 3 rules given

a objective interestingness is
given for all 3

a subjective interestingness
attempted

b Comment makes sense.
5-6
a Support or confidence not
clear
a < 3 rules given

a objective interestingness is
incomplete

a subjective interestingness is
incomplete

b Comment cursory.
0-4
Required information not
provided and/or incorrect or
misleading, demonstrating
lack of engagement with the
problem
4. Simple
classification
10 9-10
Explanation of Opinionated
demonstrates understanding
of problem.

Deep understanding of the 4
models demonstrated
thorough analysis of
performance on the task.
7-8
a correctly explains why
definition of Opinionated
makes it seem easy

b 4 confusion matrixes
given
b confusion matrixes
explained in terms of the
data and the method and
the model learnt.

c evidence of
understanding what the
models are doing
c reasoning for comparative
performance demonstrating
understanding of the
methods behind them



5-6
a partially explains why
definition of Opinionated
makes it seem easy

b 4 confusion matrixes
given
b confusion matrixes
explained at face value only

c partial understanding of
learnt models
c comparative performance
only cursorily presented
c reason for comparative
performance is shallow
0-4
a inadequate explanation

b confusion matrix
missing or misunderstood.

c Interpretation of confusion
matrix missing or faulty

c little understanding of
what the models are doing

c missing or unexplained
comparative analysis


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Criteria Max Exemplary Excellent Good Acceptable Unsatisfactory
5. Prediction 20 17-20
Approach to problem
demonstrates serious effort
to produce good results and
a deep understanding of
the relative benefits of the
2 methods in the context of
the problem domain.

Results are interpreted in
the context of the problem
domain.
14-16
a justification for choice
shows understanding of the
comparative benefits of
each and extensive
experiments.

b parameter variations
shows a combination of
experimentation and
understanding of the
parameters

c several subjective and
objective evaluation
measures used as
appropriate to method
including synthesised
evaluation

c justification for stopping
demonstrates awareness
of appropriateness of best
result and scope of
potential for further
improvement
12-13
a justification for choice
shows understanding of
the comparative benefits
of each and experiments
with performance.

b parameter variations
shows a combination of
experimentation and
understanding of the
parameters.

c multiple subjective
and objective evaluation
measures used as
appropriate to method

c justification for stopping
demonstrates awareness of
appropriateness of best
result














10-11
a justification for choice
shows some understanding
of the comparative benefits
of each or experiments
with performance.

b parameter variation
demonstrates some
experimentation

c cursory evaluation given

c justification for stopping
perfunctory
0-9
a weak justification for choice

b variation insufficient

c evaluation fails to
demonstrate effort or
understanding of evaluation

c justification for stopping
effectively absent

8

Criteria Max Exemplary Excellent Good Acceptable Unsatisfactory
6. Complex
Classification
30 26-30

Exemplary use of
classification methods with
comprehensive and fit-for-
purpose performance
analysis on the problem that
includes meaningful
reflection over the three
methods.



22-25
a explanation sound

b,c,d parameter variation
clear and extensive
demonstrating
understanding of effect in all
3 methods

b.c.d error matrix and ROC
correctly interpreted
in all 3 methods

b,c,d extensive use of
specific evaluation methods
and significance clearly
explained in all 3 methods
18-21
a explanation sound

b parameter variation
clear and sufficient for
good results
b error matrix correctly
interpreted
b ROC correctly
interpreted
b some specific evaluation
methods used

c parameter variation clear
and sufficient for good
results
c error matrix correctly
interpreted
c ROC correctly
interpreted
c some specific evaluation
methods used

d parameter variation clear
and sufficient for good
results
d error matrix correctly
interpreted
d ROC correctly
interpreted
d some specific evaluation
methods used





15-17
a satisfactory approach to
dataset partitioning

b parameter variation
perfunctory
b error matrix given
b ROC given
b few specific evaluation
methods used well


c parameter variation
perfunctory
c error matrix given
c ROC given
c few specific evaluation
methods used well


d parameter variation
perfunctory
d error matrix given
d ROC given
d few specific evaluation
methods used well
0-14
a explanation incorrect or
unsound use of
training/testing/validation
data

b no parameter
variation
b no error matrix
b no or faulty ROC
b specific evaluation
methods missing

c no parameter variation
c no error matrix
c no or faulty ROC
c specific evaluation
methods missing

d no parameter
variation
d no error matrix
d no or faulty ROC
d specific evaluation
methods missing
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Criteria Max Exemplary Excellent Good Acceptable Unsatisfactory
7. Clustering 10 9-10
The application of k-means
algorithm to the dataset and
its evaluation demonstrates
exemplary understanding of
the algorithm, its evaluation,
and its limitations.

Suitable evaluation methods
or clustering experiments in
addition to those required
here may be used.


7-8
a Convincing
justification for k

b Measure calculated
correctly. Discussion
recognises value and
limitations

c Discussion on centres
reflects numeric results and
emphasises the interesting
parts that relate to the
significance in domain terms

d Correct scatterplot
included and description
shows understanding linked
to data domain

5-6
a Justification offered
but not clear or
unconvincing

b Measure calculated
correctly

c Discussion on centres
reflects numeric results

d Correct scatterplot
included. Attempt at
influences.

0-4
Clustering experimentation
and discussion inadequate
8. Qualitative
Summary
10 9-10
Many aspects of evaluation
are discussed and a clear
conclusion is drawn, with
direct reference to the
purpose of the data
collection.

Proposal for further
investigation demonstrates
creativity and thoughtful
engagement with the
problem, clearly building on
the work reported.
8
A clear conclusion is drawn
from the work reported and a
defended proposal for
further investigation is
proposed, with clear links to
both the work reported and
the domain of application.
7
A rounded, balanced
summary of the work is
presented with a justified
proposal given.
5-6
A summary of the work is
presented and a proposal
made.
0-4
Answer does not
demonstrate adequate
engagement with the
problem nor a qualitative
understanding of the work
reported.




































































































































































































































































































































































































































































































































































































































































































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