Print View: INFS4203 - Sem 2 2009 - St Lucia

1. General Course Information

1.1 Course Details

Course Code: INFS4203 Course Title: Data Mining
Coordinating Unit: School of Information Technology and Electrical Engineering
Semester: Semester 2, 2009 Mode: Internal
Level: Undergraduate
Location: St Lucia
Number of Units: 2 Contact Hours Per Week: 2L1T
Pre-Requisites: INFS2200
Incompatible: INFS7203

Course Description: Techniques used for data cleaning, finding patterns in structured, text and web data; with application to areas such as customer relationship management, fraud detection & homeland security.

Assumed Background:

Students are assumed to have knowledge covered in INFS2200/INFS7903 Relational Databases and MATH1061/MATH7861 Discrete Mathematics. Although COMP3506/COMP7505 Algorithms and Data Structures is not the prerequisite of this course, it is desirable for students to have taken this course in their early studies.

1.2 Course Introduction

INFS4203/INFS7203 Data Mining course introduces the concepts and techniques in Data Mining and Knowledge Discovery from Databases. The students who take this course are expected to have already familiar with concepts of databases, algorithms and data structures. This course will build up a good understanding about what we can do about very large volumes of data. The knowledge gain in this course will help students to understand the fundamental technology and the theories of how we are currently dealing with the information overload - this imminent problem brought to us by the Internet. The lectures are designed to discuss the problems and ideas in data mining processes. The lectures are important for students to gain the insight of the research problems in data mining. The reading week is designed to give student time to read through the detailed knowledge and prepare for the middle semester examination. The tutorials are used to give students to apply the learned algorithms on simplified real problems. The exams (MSE and Final) are designed to give students feedbacks and the evaluation of their understanding on the data mining techniques and theory.

1.3 Course Staff

Course Coordinator: Assoc Prof Xue Li
Phone: 3365 2379 Email: xueli@itee.uq.edu.au
Campus: St Lucia Building: General Purpose South (Map) Room: 650
Consultation: Thursday 1-2pm
Other Location: Australia

Lecturer: Dr Heng Tao Shen
Phone: 3365 8359 Email: h.shen@uq.edu.au
Campus: St Lucia Building: General Purpose South (Map) Room: 651
Consultation: TBA

1.4 Timetable

Timetables are available on mySI-net.

2. Aims, Objectives & Graduate Attributes

2.1 Course Aims

The focus of this course is on to provide a comprehensive introduction to data mining. The areas covered include data quality issues of pre-processing and cleaning, predictive modelling, association analysis, clustering, text mining, web mining and time series mining. The 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.

2.2 Learning Objectives

After successfully completing this course you should be able to:

1  Be able to design algorithms to solve problems related to classifications and clustering, as well as identify association rules from a database.
2  Understand the applicability of different data mining and KDD (Knowledge Discovery from Databases) algorithms.
3  Understand the process of data mining and KDD.
4  Understand the concepts and algorithms of Text Mining and Web Mining.
5  Understand the importance of data quality issues such as pre-processing and cleaning.
6  Compare and contrast the performances of different data mining algorithms
7  Understand the scalability of data mining algorithms.
8  Understand the fundamental problems in a dataset that affect the data mining effectiveness.
9  Understand issues on the evaluations of data mining and KDD algorithms.
10  Understand limitations of data mining and KDD algorithms

2.3. Graduate Attributes

Successfully completing this course will contribute to the recognition of your attainment of the following UQ (Undergrad Pass) graduate attributes:

GRADUATE ATTRIBUTE	LEARNING OBJECTIVES
*A. IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY*
A1. A comprehensive and well-founded knowledge in the field of study.	1, 2, 3, 4, 5, 6, 7, 8, 9
A4. An understanding of how other disciplines relate to the field of study.	1, 3, 4, 5, 6, 7, 8, 9
A5. An international perspective on the field of study.	5, 6, 7, 8, 9
*B. EFFECTIVE COMMUNICATION*
B1. The ability to collect, analyse and organise information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.	1, 2, 4, 6, 8
B2. The ability to interact effectively with others in order to work towards a common outcome.	4, 7, 8, 9
B3. The ability to select and use the appropriate level, style and means of communication.	6, 7, 8
B4. The ability to engage effectively and appropriately with information and communication technologies.	8, 9
*C. INDEPENDENCE AND CREATIVITY*
C1. The ability to work and learn independently.	1, 2, 5, 8
C3. The ability to generate ideas and adapt innovatively to changing environments.	2, 5, 8
C4. The ability to identify problems, create solutions, innovate and improve current practices.	1, 2, 4, 5, 8
*D. CRITICAL JUDGEMENT*
D1. The ability to define and analyse problems.	1, 2, 4, 5, 7, 8, 9
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.	1, 2, 4, 5, 7, 8, 9
D3. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions.	1, 4, 5, 7, 8, 10
*E. ETHICAL AND SOCIAL UNDERSTANDING*
E1. An understanding of social and civic responsibility.
E2. An appreciation of the philosophical and social contexts of a discipline.
E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study.
E5. A knowledge of other cultures and times and an appreciation of cultural diversity.

Successfully completing this course will contribute to the recognition of your attainment of the following Engineers Australia graduate attributes:

GRADUATE ATTRIBUTE	LEARNING OBJECTIVES
1. Ability to apply knowledge of basic science and engineering fundamentals	1, 2, 3
2. Ability to communicate effectively, not only with engineers, but also with the community at large	3
3. In-depth technical competence in at least one engineering discipline	3
4. Ability to undertake problem identification, formulation and solution	1, 2, 10
5. Ability to utilise a systems approach to design and operational performance	2, 3
6. Ability to function effectively as an individual and in multi-disciplinary and multi-cultural teams, with the capacity to be a team leader or manager as well as an effective team member
7. Understanding of the social, cultural, global and environmental responsibilities of the professional engineer, and for the need for sustainable development	10
8. Understanding of the principles of sustainable design and development	2, 9, 10
9. Understanding of and commitment to professional and ethical responsibilities
10. Expectation and capacity to undertake life-long learning	8

3. Learning Resources

3.1 Required Resources

Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Person Addison Wesley, 2006.

Lecture Notes The Lecturer Notes will be available in PDF files before the lectures. Students must print out their own copies.

3.2 Recommended Resources

Students are not expected to purchase the following book, but may find them useful. Copies are available in the library. Data Mining Concepts and Techniques, by Jiaweihan, Micheline Kamber, Morgan Kaufmann Publishers, 2001

Students are not expected to purchase the following book, but may find them useful. Copies are available in the library. Data Mining Introductory and Advanced Topics, by Margaret H. Dunham, Prentice Hall, 2003

3.3 University Learning Resources

Access to required and recommended resources, plus past central exam papers, is available at the UQ Library website (http://library.uq.edu.au/search/r?SEARCH=INFS4203).

The University offers a range of resources and services to support student learning. Details are available on the myServices website (https://student.my.uq.edu.au/).

3.4 School of Information Technology and Electrical Engineering Learning Resources

Students enrolled at St Lucia who wish to retain a hard copy of this profile can use the free print quota provided each semester to students enrolled in courses in the School of Information Technology & Electrical Engineering. For information on how to use this print quota, see the School Policy on Student Photocopying and Printing (St Lucia) (http://www.itee.uq.edu.au/about_ITEE/policies/copy-print.html).

ITEE course websites can be found at http://www.itee.uq.edu.au/~COURSECODE. Many ITEE courses also have Usenet newsgroups, named uq.itee.COURSECODE. Instructions for accessing newsgroups are available at http://studenthelp.itee.uq.edu.au/faq/1stYearFAQ.html#accessnews.

3.5 Other Learning Resources & Information

Newsgroup

The course newsgroup is uq.itee.infs4203. This group is available on both the University and School news servers (news.uq.edu.au and news.itee.uq.edu.au).

Students are free to post questions (and answers!) to the newsgroup. Copies of announcements may also be posted to the newsgroup. The teaching staff will monitor the newsgroup.

4. Teaching & Learning Activities

4.1 Learning Activities

Date	Activity	Learning Objectives
30 Jul 09 14:00 - 30 Jul 09 15:50	Introduction to Data Mining and Data Pre-processin (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	3, 5, 7, 9, 10
6 Aug 09 14:00 - 6 Aug 09 15:50	Association Rules Mining (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	1, 7, 8, 10
13 Aug 09 14:00 - 20 Aug 09 15:50	Classification (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	2, 3, 6, 7, 9, 10
27 Aug 09 14:00 - 3 Sep 09 15:50	Clustering (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	1, 7, 9, 10
10 Sep 09 14:00 - 10 Sep 09 15:50	Revision of Previous Topics (Self Directed Learning): Read the materials that are related to the middle semester examination. Readings/Ref: Required Text ; Lecture Notes ; Reference Texts ; Reference Texts	1, 2, 3, 5, 6, 7, 8, 9, 10
17 Sep 09 14:00 - 17 Sep 09 15:50	Middle Semester Exam (Progressive Exam): 1:30 Hrs Middle Semester Examp to be held during the lecture time. Readings/Ref: Required Text ; Lecture Notes ;	1, 2, 3, 5, 6, 7, 8, 9, 10
24 Sep 09 14:00 - 8 Oct 09 15:50	Advanced Topic I -- Text and Web Mining (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	2, 4, 5, 7, 8, 9, 10
15 Oct 09 14:00 - 15 Oct 09 15:50	Advanced Topic II -- Time Series Mining (Lecture): Readings/Ref: Required Text ; Lecture Notes ; Reference Texts	1, 2, 6, 9, 10
22 Oct 09 14:00 - 22 Oct 09 15:50	Revision of Previous Topics (Self Directed Learning): Read the materials that are related to the middle semester examination. Readings/Ref: Required Text ; Lecture Notes ; Reference Texts ; Reference Texts	1, 2, 3, 5, 6, 7, 8, 9, 10
29 Oct 09 10:00 - 29 Oct 09 11:50	Course Revision (Lecture): Readings/Ref: Required Text ; Lecture Notes ;	2, 3, 4, 5, 6, 7, 8, 9, 10

4.2 Other Teaching and Learning Activities Information

This course is mainly conducted by lectures and the students will be give tutorial questions after lectures. The assignments are designed to be problem-solving.

5. Assessment

5.1 Assessment Summary

This is a summary of the assessment in the course. For detailed information on each assessment, see 5.5 Assessment Detail below.

Assessment Task	Due Date	Weighting	Learning Objectives
Exam - during Exam Period (School) Final Examination	Examination Period	60%	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Work-based Assessment Individual Assignmnets	21 Aug 09 - 8 Oct 09 Assignments on Weeks 4, 7, 10, and 13	20% (5% x 4 assignments)	2, 3, 4, 5, 6, 8, 9, 10
Exam - Mid Semester During Class Middle Semester Exam	17 Sep 09 14:00 - 17 Sep 09 15:40 Non-programmable calculator is required.	20%	1, 2, 3, 4, 5, 6, 7, 8, 9, 10

5.2 Course Grading

Grade 1, Fail: Fails to demonstrate most or all of the basic requirements of the course: 0 - 29

Grade 2, Fail: Demonstrates clear deficiencies in understanding and applying fundamental concepts; communicates information or ideas in ways that are frequently incomplete or confusing and give little attention to the conventions of the discipline: 30 - 49

Grade 3, Fail: Demonstrates superficial or partial or faulty understanding of the fundamental concepts of the field of study and limited ability to apply these concepts; presents undeveloped or inappropriate or unsupported arguments; communicates information or ideas with lack of clarity and inconsistent adherence to the conventions of the discipline: 40 - 49

Grade 4, Pass: Demonstrates adequate understanding and application of the fundamental concepts of the field of study; develops routine arguments or decisions and provides acceptable justification; communicates information and ideas adequately in terms of the conventions of the discipline: 50 - 64

Grade 5, Credit: Demonstrates substantial understanding of fundamental concepts of the field of study and ability to apply these concepts in a variety of contexts; develops or adapts convincing arguments and provides coherent justification; communicates information and ideas clearly and fluently in terms of the conventions of the discipline: 65 - 74

Grade 6, Distinction: As for 5, with frequent evidence of originality in defining and analysing issues or problems and in creating solutions; uses a level, style and means of communication appropriate to the discipline and the audience: 75 - 89

Grade 7, High Distinction: As for 6, with consistent evidence of substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critically evaluates problems, their solutions and implications: 90 - 100

5.3 Late Submission

No extensions will be granted except in exceptional personal circumstances (documented medical reason or family emergency). Personal hardware or computer failures are not grounds for extension.

5.5 Assessment Detail

Final Examination

Type: Exam - during Exam Period (School)
Learning Objectives Assessed: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Due Date:
Examination Period
Weight: 60%
Perusal: 10 minutes
Duration: 120 minutes
Format: Short answer, Problem solving
Task Description:

Cover all topics in the Lecture Notes.

Individual Assignmnets

Type: Work-based Assessment
Learning Objectives Assessed: 2, 3, 4, 5, 6, 8, 9, 10
Due Date:
21 Aug 09 - 8 Oct 09 Assignments on Weeks 4, 7, 10, and 13
Weight: 20%
(5% x 4 assignments)
Task Description: Individual assignments to assess the learning of students in this course.
Criteria & Marking:

Each individual assignment assesses the understanding of the students on different processes of data mining. In particuar, four topics would be assessed: (1) Association rule mining process; (2) Classification process; (3) Clustering process; (4) Text Mining Process.

Middle Semester Exam

Type: Exam - Mid Semester During Class
Learning Objectives Assessed: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Due Date:
17 Sep 09 14:00 - 17 Sep 09 15:40 Non-programmable calculator is required.
Weight: 20%
Perusal: 10 minutes
Duration: 90 minutes
Format: Short answer, Problem solving
Task Description: Middle Semester Exam will include the contents covered by week 1-6 (inclusive) weekly topics.
Criteria & Marking: The questions are of the similar style of the tutorial questions.

6. Policies & Guidelines

This section contains the details of and links to the most relevant policies and course guidelines. For further details on University Policies please visit myAdvisor and the University Handbook of Policies and Procedures.

6.1 Assessment Related Policies and Guidelines

University Policies & Guidelines

An overview of the University’s assessment-related policies can be found on myAdvisor (http://www.uq.edu.au/myadvisor/index.html?page=2910).

Academic Integrity
It is the University's task to encourage ethical scholarship and to inform students and staff about the institutional standards of academic behaviour expected of them in learning, teaching and research. Students have a responsibility to maintain the highest standards of academic integrity in their work. Students must not cheat in examinations or other forms of assessment and must ensure they do not plagiarise.

Plagiarism
The University has adopted the following definition of plagiarism:

Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.

Students are encouraged to read the UQ Academic Integrity and Plagiarism policy (http://www.uq.edu.au/hupp/index.html?page=25128) which makes a comprehensive statement about the University's approach to plagiarism, including the approved use of plagiarism detection software, the consequences of plagiarism and the principles associated with preventing plagiarism.

Feedback on Assessment
Feedback is essential to effective learning and students can expect to receive appropriate and timely feedback on all assessment. For a detailed explanation of the feedback you are entitled to, you should consult the policy on Student Access to Feedback on Assessment. (http://www.uq.edu.au/hupp/index.html?page=25109)

As a student you have a responsibility to incorporate feedback into your learning; make use of the assessment criteria that you are given; be aware of the rules, policies and other documents related to assessment; and provide teachers with feedback on their assessment practices.

There are certain steps you can take if you feel your result does not reflect your performance. Please refer to the myAdvisor web site. (http://www.uq.edu.au/myadvisor/index.html?page=2953)

School of Information Technology and Electrical Engineering Assessment Guidelines

Misconduct

Further to the statement on academic integrity and plagiarism above, students are required to read and understand the ITEE policy on Student Misconduct (http://www.itee.uq.edu.au/about_ITEE/policies/student-misconduct.html).

Late Arrival or Non-attendance at Examinations

The policy and procedure for late arrival or non-attendance at centrally controlled and school-based examinations is set out in the University's Assessment policy (HUPP 3.30.1), section 4.8 at http://www.uq.edu.au/hupp/index.html?page=25109.

In the case that a student requests a special exam for a School-controlled exam, the request will be considered and, if allowed, the timing shall be determined by the course coordinator, in consultation with the School's Chief Examiner where necessary, and in accordance with HUPP 3.30.1. Unless otherwise indicated in the Course Profile, applications must be made in writing to the Head of School no later than 5 days after the exam. Late applications will not be accepted.

Examination Feedback

In addition to the advice above, students wishing to view examination answer scripts and/or question papers should consult with the School office (Room 217, General Purpose South Building [78], St Lucia) regarding arrangements. The ITEE policy on exam script viewing is available at http://study.itee.uq.edu.au/current_students/exam_script_viewing.html.

Supplementary Assessment

If you fail this course you may be eligible for supplementary assessment - see the general award rules and/or your program rules for details. You should note that even though you may be eligible for supplementary assessment under these rules, in some circumstances there may be no practical assessment that can be offered to allow you to meet the minimum passing requirements. These circumstances may include failure based on:

group or team based assessment;
attendance or class participation requirements;
laboratory-based assessment, where laboratories can't practically be made available after classes have finished;
project or thesis-based assessment, where a significant period of time would be required to undertake supplementary assessment;
progressive assessment, where subsequent assessment items build on earlier assessment items; or
multiple assessment items, where it is impractical to offer multiple supplementary assessment items.

If the course coordinator determines that there is no practical supplementary assessment that can be offered to allow you to improve your grade, then you will not be offered supplementary assessment and your grade will remain unchanged.

Calculators in Examinations

Some examinations in the School of Information Technology and Electrical Engineering restrict the type of calculator that can be used. If this course profile does not specify any calculator restrictions, you should check with the course coordinator as to whether any restrictions apply. In some examinations, you may only be permitted to use an EPSA/EAIT approved and labelled non-programmable calculator. It is your responsibility to ensure you have a suitable approved and labelled calculator if required.

6.2 Other Policies and Guidelines

University Policies and Guidelines

Placement Courses
Students on a placement course – also known as a work placement, internship, industry study, industry experience, clinical practice, clinical placement, practical work, practicum, fieldwork, teaching practice – should refer to the University policy, Placement Courses (http://www.uq.edu.au/hupp/index.html?page=25120) for detailed information.

Working with Children
Students whose studies include a professional/work placement, internship, clinical practice, teaching practice or other similar activity which involves them in regular contact with children should refer to the University policy, Working with Children Check - "blue card" (http://www.uq.edu.au/hupp/index.html?page=25004) to find out how to apply for a ‘blue card’.

Students with a Disability
Any student with a disability who may require alternative academic arrangements, including assessment, in the course/program is encouraged to seek advice at the commencement of the semester from a Disability Adviser at Student Support Services. Refer to the University policy, Students with a Disability (Disability Action Plan) (http://www.uq.edu.au/hupp/index.html?page=25122) and to the policy on Special Arrangements for Examinations for Students with a Disability (http://www.uq.edu.au/hupp/index.html?page=25111

Where an adjustment is made to an accredited program, it is the responsibility of the relevant Faculty to liaise with professional and registration bodies regarding the acceptability of the change/s.

Occupational Health and Safety
Undergraduate Students (http://www.uq.edu.au/hupp/index.html?page=25055) and Postgraduate Students (http://www.uq.edu.au/hupp/index.html?page=25057) should be familiar with the University policies on occupational health and safety in the laboratory.

Other School of Information Technology and Electrical Engineering Guidelines

Ethical Clearance

If your course involves assignment or project work involving human subjects or human-related materials, you must investigate the need for ethical clearance and obtain it when required. Information on ethical clearance can be found at http://www.uq.edu.au/research/orps/index.html?page=5064&pid=5256.

Learning Summary

Below is a table showing the relationship between the learning objectives for this course and the broader graduate attributes developed, the learning activities used to develop each objective and the assessment task used to assess each objective.

Learning Objectives