Course coordinator
Dr Miao Xu
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Techniques used for data cleaning, finding patterns in structured, text and web data; with application to areas such as customer relationship management, fraud detection and homeland security.
This course introduces the concepts and techniques in Data Mining and Knowledge Discovery from Databases. Students taking this course are expected to be already familiar with concepts of databases, algorithms and data structures. This course will provide a good introduction to analysing large volumes of data. The lectures are designed to discuss the problems and solutions in data mining including data classification and clustering, anomaly detection, mining of association rules, and data mining withﾠtext and Web data.
Course changes in response to previous student feedback: Advanced materials are added to the course to account for the interest among students, allowing for deeper exploration into complex data mining techniques and their applications in real-world scenarios.
Course requirements
Assumed background
Students are assumed to have the background knowledge covered in INFS2200/INFS7903 (Relational Database Systems) and CSSE1001/7030 (Introduction to Software Engineering).ﾠWhile COMP3506/COMP7505ﾠ(Algorithms and Data Structures) is not a prerequisite of this course, it is beneficial for students to have taken that course in their early studies.ﾠ ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(INFS7901 or INFS7903) and CSSE7030
Incompatible
You can't enrol in this course if you've already completed the following:
INFS4203
Jointly taught details
This course is jointly-taught with:
- INFS4203
All learning activities are jointly taught.
Course contact
Course staff
Lecturer
Dr Miao Xu
Dr Zijian Wang
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Tutorials will start from Week 2.
Wednesday 14 August is a public holiday and no tutorial on this day. The tutorial recording will be provided this week (Week 4) to aid students who attend regularly the Wednesday tutorial sessions.
Aims and outcomes
The focus of this course is to provide a comprehensive introduction to data mining. The areas covered include association analysis,ﾠclassification, clustering, text mining and web mining.ﾠThe goal is to present fundamental concepts and algorithms for each topic, thus providing the students with the necessary background for the application of data mining to real problems. In addition, this course also provides a starting point for those students who are interested in pursuing research in data mining or related fields.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Identify the process of data mining and KDD (Knowledge Discovery from Databases).
LO2.
Analyze the applicability of different data mining and KDD algorithms.
LO3.
Design algorithms to solve problems related to classifications and clustering, as well as identify association rules from a database.
LO4.
Apply the concepts and algorithms of text mining and web mining.
LO5.
Evaluate the performance of data mining and KDD algorithms.
LO6.
Compare and contrast the performances of different data mining algorithms
LO7.
Evaluate the scalability of data mining algorithms.
LO8.
Analyze the data characteristics that affect the effectiveness of data mining.
LO9.
Examine the limitations of data mining and KDD algorithms
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
In-Class Quiz
|
15% |
3/09/2024 3:30 pm
The quiz is scheduled to be from 14:00 to 15:30 during the scheduled lecture time in week 7, with a reading time of 10 minutes and an exam time of 80 minutes. |
| Paper/ Report/ Annotation, Project | Project Proposal | 15% |
13/09/2024 4:00 pm |
| Computer Code, Paper/ Report/ Annotation, Project | Project Report | 20% |
25/10/2024 4:00 pm |
| Examination |
Final Examination
|
50% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
In-Class Quiz
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 15%
- Due date
3/09/2024 3:30 pm
The quiz is scheduled to be from 14:00 to 15:30 during the scheduled lecture time in week 7, with a reading time of 10 minutes and an exam time of 80 minutes.
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L05, L06, L08, L09
Task description
- This is an online non-invigilated quiz implemented in the form of a Blackboard quiz. Students will complete the quiz online via Blackboard at the same time.
- It is an individual assessment, covering lecture contents from Week 2 to Week 6.
- The format of the quiz will be in the form of multiple-choice questions.
- Course staff will be available via Zoom to answer clarification questions about the quiz.
- Pre-approval is required if a special arrangement is needed.
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Project Proposal
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 15%
- Due date
13/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L05, L07, L08, L09
Task description
1. The assignment is designed to test the ability to apply data mining techniques to solve real-world problems.
2. This is an individual assignment. You will be required to complete the proposal on your own design. The proposal is required to be submitted via Blackboard in week 8 (4pm Friday).
3. The assignment dataset and specification will be released in week 2 on Blackboard.
Submission guidelines
Please refer to the project specification for details.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach, where feedback or detailed solutions will be released to students within 14-21 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Project Report
- Mode
- Activity/ Performance
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
25/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L05, L06, L07, L08, L09
Task description
1. The assignment is designed to test the ability to implement data mining techniques to solve real-world problems.
2. This is an individual assignment. You will be required to complete the coding and report based on your design. Report and all code compressed into one file are required to be submitted via Blackboard by 4 pm Friday of week 13.
3. The assignment and detailed criteria and marking sheet will be released in week 9 on Blackboard.
Submission guidelines
Please refer to the project specification for details.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
This course uses a progressive assessment approach, where feedback or detailed solutions will be released to students within 14-21 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Examination
- Hurdle
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 50%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L08, L09
Task description
This course will have one final exam. The minimum 50% (25 marks) must be obtained in the final exam to pass this course.
Delivery Mode: On-campus invigilated exam, closed book.
Timing: The final exam will be scheduled at a fixed time for all students – i.e. students will complete the exam simultaneously.
Permitted materials: a Casio FX82 series or UQ approved and labeled calculator, unrestricted number of blank A4 sheets of blank paper.
More information will be released in the last lecture (i.e., the course revision).
Hurdle requirements
Students must receiveﾠa passing grade on the final exam in order to pass this course (i.e., achieve at least 50% of the final exam). If you fail the exam, your final mark will be capped at 49 and your final grade will be capped at 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Students must receiveﾠa passing grade on the final exam in order to pass this course (i.e., achieve at least 50% of the final exam). If you fail the exam, your final mark will be capped at 49 and your final grade will be capped at 3.
Overall marks will be rounded to the nearest integer before grade cut-offs are applied.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Using Artificial Intelligence (AI) and Machine Translation (MT) for Project Proposal
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI in a revision of existing authentic assessments only. Students must clearly reference any use of AI or MT in each instance to complete the assessment task.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. Students may be required to demonstrate detailed comprehension of their written submission independent of AI tools through an in-person interview.
Using Artificial Intelligence (AI) and Machine Translation (MT) for other assessment tasks
These assessment tasks evaluate students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Expectations for Graduate Students in Assessments
Graduate students are expected to have an in-depth understanding of topics within data mining and the ability to critically evaluate existing methodologies. The final exam for graduate students will emphasize more on advanced analytical, critical thinking, and problem-solving skills.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lectures The course lectures will provide in-depth knowledge of various concepts and techniques in Data Mining Techniques. Lecture notes will be available for all the material covered in this course. Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09 |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Contacts Starting from Week 2, Tutorials will provide an opportunity for further practicing the concepts introduced in the lectures via examples, exercises and problem-solving. Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08, L09 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: