Data Mining and Knowledge Discovery
Autumn Session 2022
Wollongong, South Western Sydney
On Campus
UOW may need to change teaching locations and/or teaching delivery at short
notice to ensure the safety and well-being of students and staff in response to the
COVID-19 pandemic or other public health requirements.
Credit Points: 6
Pre-requisites: Nil
Co-requisites: Nil
Equivalences (or
not to count
with):
INFO411
2 | INFO911 SUBJECT OUTLINE AUTUMN SESSION 2022
The original material prepared for this guide is covered by copyright. Apart from fair dealing for the purposes of private study,
research, criticism or review, as permitted under the Copyright Act 1968 (Cth), no part may be reproduced by any process
without written permission.
4 | INFO911 SUBJECT OUTLINE AUTUMN SESSION 2022
Section A: Subject Information
Consultation Times Tuesday 09:30 - 11:30 (subject to change)
Wednesday 09:30 - 11:30 (subject to change)
Subject Coordinator
Name Professor Lei Wang
Telephone 42213771
Email leiw@uow.edu.au
Room 3.219
Consultation Times Tuesday 09:30 - 11:30
Wednesday 09:30 - 11:30
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SUBJECT DETAILS
Subject Description
Introduction to Data Mining, Knowledge Discovery, and Big Data with coverage of Data Structures, role of Data
Quality and per-processing, Association Rules, Artificial Neural Networks, Support Vector methods, Tree Based
Methods, Clustering and Classification Methods, Regression and Statistical Methods, Overfitting and Inferential
issues, Evaluation, Use of Data Mining packages with applications for benchmark and real world situations.
Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. Identify useful relationships and important subgroups in large data sets.
2. Suggest appropriate approaches and solutions to given data mining problems.
3. Plan and carry out analyses of large and complex data sets.
4. Use parametric, non-parametric, and probabilistic methods to model data in various domains.
5. Analyse and interpret results
6. Use data mining software such as R as well as use relevant plugins and software packages.
7. Analyse data mining algorithms and techniques.
8. Understand the role and challenges of methods in Big Data applications.
9. Identify and distinguish data mining applications from other IT applications.
10. Describe data mining algorithms.
11. Compare the applicability of data mining applications.
Assessment Summary
No. Assessment Name
Assessment
Weight
Mapping to Subject Learning
Outcome Task Due
1 Individual Assignment 15%
SLO1, SLO3, SLO4, SLO5,
SLO8, SLO11
15 Apr 2022 (Friday in
Session Week 7)
Final submission time:
11:59pm
2 Individual assignment 15%
SLO1, SLO3, SLO4, SLO5,
SLO8, SLO6, SLO7
27 May 2022 (Friday in
Session Week 12)
Final submission time:
11:59pm
3 Group project 20% SLO1, SLO10, SLO11, SLO3, SLO4, SLO5, SLO7, SLO8, SLO2
15 May 2022 (Sunday in
Session Week 10)
Final submission time:
11:59pm
4 Final Exam 50% SLO10, SLO11, SLO8, SLO9 UOW Exam Period
Detailed assessment information is available in Section B of the subject outline.
Student Workload
Students should note that UOW policy equates 1 credit point with 2 hours of study per week, including lectures
and tutorials/workshops/practicals, self-directed study and work on assessment tasks. For example, in a 6 credit
point subject, a total of 12 hours of study per week is expected.
Subject Changes and Response to Student Feedback
The School is committed to continual improvement in teaching and learning and takes into consideration student
feedback from many sources. These sources include direct student feedback to tutors and lecturers, feedback
through Student Services and the Faculty Central, and responses to the Subject Evaluation Surveys. This
information is also used to inform comprehensive reviews of subjects and courses.
6 | INFO911 SUBJECT OUTLINE AUTUMN SESSION 2022
Extraordinary Changes to the Subject Outline
In extraordinary circumstances the provisions stipulated in this Subject Outline may require amendment after the
Subject Outline has been distributed. All students enrolled in the subject must be notified and have the opportunity
to provide feedback in relation to the proposed amendment, prior to the amendment being finalised.
Learning Analytics
"Where Learning Analytics data (such as student engagement with Moodle, access to recorded lectures,
University Library usage, task marks, and use of SOLS) is available to the Subject Coordinator, this may be used
to assist in analysing student engagement, and to identify and recommend support to students who may be at risk
of failure. If you have questions about the kinds of data the University uses, how we collect it, and how we protect
your privacy in the use of this data, please refer to https://www.uow.edu.au/about/privacy/index.html".
Your Privacy - Lecture Recording
In accordance with the Student Privacy & Disclosure Statement, when undertaking our normal teaching and
learning activities, the University may collect your personal information. This collection may occur incidentally
during the recording of lectures in equipped venues (i.e., when your identity can be ascertained by your image,
voice or opinion), therefore the University further advises students that:
Lecture recordings are made available to students, university staff, and affiliates, securely on the
university's Echo360 ALP (Active Learning Platform) and via the subject Moodle eLearning site;
Recordings are made available only for which they were recorded, for example, as a supplemental
study tool or to support equity and access to educational resources;
Recordings are stored securely for up to four years.
If you have any concerns about the use or accuracy of your personal information collected in a lecture recording,
you may approach your Subject Coordinator to discuss your particular circumstances.
The University is committed to ensuring your privacy is protected. If you have a concern about how your
personal information is being used or managed please refer to the University's Privacy Policy or consult our
Privacy webpage https://www.uow.edu.au/privacy/
Additional Information About This Subject
Not applicable.
ELEARNING, READINGS, REFERENCES AND MATERIALS
Subject eLearning
The University uses the eLearning system Moodle to support all coursework subjects. To access eLearning you
must have a UOW user account name and password, and be enrolled in the subject. eLearning is accessed via
SOLS (Student Online Service). Log on to SOLS and then click on the eLearning link in the menu column.
The University is committed to providing a safe, respectful, equitable and orderly environment for the University
community, and expects each member of that community to behave responsibly and ethically. Students must
comply with the University's Student Conduct Rules and related policies including the IT Acceptable Use Policy
and Bullying Prevention Policy, whether undertaking their studies face-to-face, online or remotely. For more
information on appropriate communication and etiquette in the online environment please refer to the guide Online
and Email Etiquette.
Major Text
Main textbook:
[1] Pang-Ning Tan, Micheale Steinbach, Vipin Kumar, "Introduction to Data Mining", Addison Wesley, 2006,
ISBN 0-321-32136-7
Other textbooks:
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[2] A. B. M. Shawkat Ali, Saleh A. Wasimi, "Data Mining:Methods and Techniques", Thomson, 2007, ISBN
978-0-17-013676-1
[3] Ian H. Witten, Eibe Frank "Data Mining Practical Machine Learning Tools and Techniques", Elsevier inc.,
2005, ISBN 0-12-088407-0
[4] Jiawei Han, Micheline Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann publishers,
2006, ISBN 978-1-55860-901-3
[5] Margaret Dunham, "Data Mining Introductory and Advanced Topics, Pearson Education Inc., 2003, ISBN0-
13-088892-3
[6] Graham Williams , "Data Mining with Rattle and R: the art of excavating data for knowledge discovery",
Springer Verlag, 2011, ISBN 9781441998903.
Recommended Readings
Students are encouraged to use the UOW Library catalogue and databases to locate additional resources including
the e-readings list: https://ereadingsprd.uow.edu.au/
References
Any readings/references are recommended only.
This is not an exhaustive list. Students are encouraged to use the UOW Library catalogue and databases to locate
additional resources.
Other Resources
Not applicable.
Additional Requirements / Materials to be Purchased
Not applicable.
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LECTURES AND OTHER LEARNING ACTIVITIES
Lecture and Contact Hours
UOW may need to change teaching locations and/or teaching delivery at short notice to ensure the safety and well-being of students and staff in response to the COVID-19
pandemic or other public health requirements.
Current timetable information is located at https://www.uow.edu.au/student/timetables
Minimum Attendance Requirements
Satisfactory attendance is deemed by the University, to be attendance at approximately 80% of the allocated contact hours.
Lecture Recordings
The University of Wollongong supports the recording of lectures as a supplemental study tool, to provide students with equity of access, and as a technology-enriched learning
strategy to enhance the student experience.
If you make your own recording of a lecture you can only do so with the explicit permission of the lecturer and those people who are also being recorded. You may only use
recorded lectures, whether they are your own or recorded by the university, for your own educational purposes. Recordings cannot be altered, shared or published on another
platform, without permission of the University, and to do so may contravene the University's Copyright Policy, Privacy Policy, Intellectual Property Policy, IT Acceptable Use
Policy and Student Conduct Rules. Unauthorised sharing of recordings may also involve a breach of law under the Copyright Act 1969.
Most lectures in this subject will be recorded, when they are scheduled in venues that are equipped with lecture recording technology, and made available via the subject Moodle
site with 48 hours.
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Lecture Schedule
This is a guide to the weekly lecture topics however the delivery date of these topics may on occasion vary due to unforeseen circumstances, such as the availability of a guest
lecturer or access to other resources.
Week Beginning Lecture Topics Tutorial/Workshop/Laboratory/Demonstration/Field Work Readings/Other subject information Task Due
Week 1
28 Feb 2022
(Monday)
Introduction to the subject No lab As advised in the lecture slides
Week 2
07 Mar 2022
(Monday)
Visual Data Mining Lab 1 As advised in the lecture slides
Week 3
14 Mar 2022
(Monday)
Clustering Lab 2 As advised in the lecture slides
Week 4
21 Mar 2022
(Monday)
Big Data Lab 3 As advised in the lecture slides
Week 5
28 Mar 2022
(Monday)
Classification and Prediction Lab 4 As advised in the lecture slides
Week 6
04 Apr 2022
(Monday)
Association Analysis Lab 5 As advised in the lecture slides
Week 7
11 Apr 2022
(Monday)
Support Vector Machines Lab 6 As advised in the lecture slides Assignment 1
18 Apr 2022 Mid-Session Recess
Week 8
25 Apr 2022
(Monday)
Decision Trees Lab 7 As advised in the lecture slides
Week 9
02 May 2022
(Monday)
Regression Lab 8 As advised in the lecture slides
Week 10
09 May 2022
(Monday)
Statistical Methods Lab 9 As advised in the lecture slides Project
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Week 11
16 May 2022
(Monday)
Group Project Presentation Lab 10 As advised in the lecture slides
Week 12
23 May 2022
(Monday)
Group Project Presentation Lab 11 As advised in the lecture slides Assignment 2
Week 13
30 May 2022
(Monday)
Subject revision No lab
06 Jun 2022 Study Recess
13 Jun 2022 Examinations
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Section B: Assessment
ASSESSMENT TASKS
Minimum Performance Requirements
To be eligible for a Pass in this subject a student must achieve a mark of at least 40% in the final exam. All
assessment tasks must be submitted.
Students who do not meet the minimum performance requirements, as specified for each assessment, will receive
a TF (Technical Fail) grade for this subject, which will appear on your Academic Transcript.
Requirements Related to Student Contributions
Labs, and projects that are marked as group work must be conducted as part of a group and by following the
specified conditions (i.e. with respect to a minimum or maximum group size). Group assessments are typically
assessed as a group product, usually with the same mark allocated to each group member. However, the subject
co-ordinator reserves the right to allocate individual marks for students for an assessment task when necessary
(for example, in cases where contributions of group members have been unequal).
Referencing
Referencing style will be specified in the Project task sheet.
Please consult the UOW Library website for further information: https://uow.libguides.com/refcite
Detailed Assessment Information
Assessment 1
Assessment
Name Individual Assignment
Assessment
Type Assignment
Weighting 15%
Subject
Learning
O utcomes
Assessed
SLO1, SLO3, SLO4, SLO5, SLO8, SLO11
Individual
or Group
Assessment
Individual
Due Date
15 Apr 2022 (Friday in Session Week 7)
Final submission time: 11:59pm
Assessment
Description and
Criteria
Correctness, completeness and consistency with specification
Length /
Duration To be advised in the assignment description
Method of
Submission
Online via Moodle
Return of
Assessed Work Week 9, Marks and comments
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