Students

COMP8230 – Mining Unstructured Data

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor and Lecturer
Yu Zhang
y.zhang@mq.edu.au
Contact via Contact via email
Lecturer
Qiongkai Xu
qiongkai.xu@mq.edu.au
Contact via Contact via email
Credit points Credit points
10
Prerequisites Prerequisites
COMP6200 or Admission to the GradDipRes or GradCertRes
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

Unstructured data, like text data, graph data, audios, and videos widely exist in our daily life. Efficiently and effectively mining the unstructured data are significant and acting as the backbone in many real applications, like machine translation, face recognition, and link prediction. This unit will introduce key concepts in unstructured data mining, including specific algorithms and techniques for unstructured data cleaning, pattern mining, knowledge discovery, and the prediction of unstructured data. By taking this unit you will be given a broad view of the general issues surrounding unstructured data and the application of methodologies and algorithms to such a type of data. You will have the chance to explore an assortment of unstructured data mining techniques, which you will apply to solve problems involved in real scenarios.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Demonstrate an understanding of basic concepts, techniques, algorithms and modellings in unstructured data mining.
  • ULO2: Identify the appropriate data mining techniques and algorithms for real life unstructured data mining problems.
  • ULO3: Explain how good decision making is supported by descriptive and predictive data mining
  • ULO4: Present and analyse the unstructured data mining results with advanced data mining techniques.
  • ULO5: Communicate clearly and effectively

General Assessment Information

General Assessment Information 

Release Dates

  • Assessment 1: To be released no later than 6th March
  • Assessment 2: To be released no later than 2th April
  • Assessment 3: To be released no later than 15th May

The University's Academic Integrity policy will be enforced. You may assist your fellow students with general concepts, pointers to resources and useful tools or commands that are publicly available. You may not become involved in any way in helping a fellow student to find the solution to their particular task, nor may you share with them any aspect of the solution of your particular task. 

Each assessment task must be the sole work of the student turning it in. Any cheating will be handled under the University's Academic Integrity Policy.

Requirements to Pass this Unit

To pass this unit you must:

  • Achieve a total mark equal to or greater than 50%.

Late Submission Policy

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. o
    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.
    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.
  • After 7 days: Submissions more than 7 days late will receive a mark of 0.
  • Extensions:
    • Short Extension: Some assessments are eligible for a short extension. You can only apply for a short extension before the due date.
    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Need help? Review the Special Consideration page HERE

Assessments where Late Submissions will be accepted 

  • Assessment 1 – YES, Standard Late Penalty applies 
  • Assessment 2 – YES, Standard Late Penalty applies 
  • Assessment 3 – YES, Standard Late Penalty applies 

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable, and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through connect.mq.edu.au.

Details for each assignment will be available via iLearn

You are encouraged to:

  • Set your personal deadline earlier than the actual one
  • Keep backups of all your important files
  • Ensure that no-one else picks up your printouts

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Problem Analysis 30% No 29/03/2026 Individual Yes Open
Report on Data Mining in Industry 30% No 10/05/2026 Individual Yes Open
Literature Review 40% No 31/05/2026 Individual Yes Open

Problem Analysis

Assessment Type 1: Written Submission
Indicative Time on Task 2: 23 hours
Due: 29/03/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

You will receive sample problem topics and are required to identify and discuss relevant data mining techniques, develop an exploration plan for your chosen problem, and produce a written report.
On successful completion you will be able to:
  • Demonstrate an understanding of basic concepts, techniques, algorithms and modellings in unstructured data mining.
  • Explain how good decision making is supported by descriptive and predictive data mining
  • Communicate clearly and effectively

Report on Data Mining in Industry

Assessment Type 1: Written Submission
Indicative Time on Task 2: 23 hours
Due: 10/05/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

You will produce a written report and deliver a presentation examining an aspect of unstructured data mining and its application in an industry context.
On successful completion you will be able to:
  • Demonstrate an understanding of basic concepts, techniques, algorithms and modellings in unstructured data mining.
  • Present and analyse the unstructured data mining results with advanced data mining techniques.

Literature Review

Assessment Type 1: Written Submission
Indicative Time on Task 2: 29 hours
Due: 31/05/2026
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open

You will undertake a review of work related to one of the topics covered in the unit and present your findings in a written literature review.
On successful completion you will be able to:
  • Demonstrate an understanding of basic concepts, techniques, algorithms and modellings in unstructured data mining.
  • Identify the appropriate data mining techniques and algorithms for real life unstructured data mining problems.
  • Communicate clearly and effectively

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Classes

Each week has a two-hour in-person lecture and a one-hour in-person workshop. For details of days, times and rooms consult the timetables webpage. 

Lectures are a core learning experience where we will discuss the key theoretical underpinnings and concepts to this unit. Lecture recordings will be available after each lecture in iLearn. 

Workshops involve working in small groups (up to four students) and provide an opportunity to consolidate your understanding of the unit’s key concepts and develop skills in analysing these concepts through case studies.

Week 1 classes: Lectures and Workshops begin in Week 1.

Required and Recommended Texts

All required and recommended readings will be provided as part of the lecture material.

Unit Web Page

The unit web page will be hosted in iLearn, where you will need to log in using your Student One ID and password. The unit will make extensive use of discussion boards also hosted in iLearn. Please post questions there, they will be monitored by the staff on the unit.

Methods of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to the unit convenor from your university email address.

Unit Schedule

Week Topic                                                       Note
1 Unstructured Data Mining in IoT    
2 Personal Health Data Mining in IoT  
3 Millimetre Wave Radar Sensing for Personal Health  
4 Localisation and Tracking in IoT  
5 Deep Learning for Mining Unstructured Data Assessment 1 due
6 Federated Learning for Data Privacy   
Recess    
7 Large Language Model in AI Agents and Society  
8 Data Fusion and Multimodal Model for Data Mining  
9 Weakly Supervised Learning for Mining Unstructured Data Assessment 2 due
10 Multiple Instance Representation Learning with Data Mapping  
11 Bag-constrained Data Mining with Multiple Views  
12 Advanced Topic of Unstructured Data Mining Assessment 3 due 
13 Unit Review   

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from teaching staff. As such, no change to the delivery of the unit is planned, however we will continue to strive to improve the level of support and the level of student engagement.

Unit information based on version 2026.03 of the Handbook