Print View: INFS7203 - Sem 2 2008 - St Lucia

1. General Course Information

1.1 Course Details

Course Code: INFS7203 Course Title: Data Mining
Coordinating Unit: School of Information Technology and Electrical Engineering
Semester: Semester 2, 2008 Mode: Internal
Level: Postgraduate Coursework
Location: St Lucia
Number of Units: 2 Contact Hours Per Week: 2L1T
Pre-Requisites: INFS7903
Incompatible: INFS4203

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 inportant for students to gain the insight of the research problems in data mining. The reading weeks (Week 6 and Week 7) are 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: Dr Xue Li
Phone: 3365 2379 Email: xueli@itee.uq.edu.au Homepage: www.itee.uq.edu.au/~xueli
Campus: St Lucia Building: General Purpose South (Map) Room: 650
Consultation:

13:00 - 14:00 Thursday

1.4 Timetable

Timetables are available on mySI-net.

Additional Timetable Information
The tutorials starts from week two, There is only one-hour weekly tutorials. There will be no tutorials in the weeks that have no lectures (i.e., weeks 6,7,8).

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 anomaly detection. 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 simple problems of 'Market Basket', classifications, clustering, sequential patterns, etc.
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 (Postgrad Coursework) graduate attributes:

GRADUATE ATTRIBUTE	LEARNING OBJECTIVES
*A. IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY*
A2. A broad understanding of the field of study, including how other disciplines relate to the field of study.	1, 2, 3, 4, 6, 7, 8
A3. A comprehensive and in-depth knowledge in the field of study.	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
A5. An international perspective on the field of study.	4, 5, 10
A7. An appreciation of the link between theory and practice.
*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, 5, 6, 7, 8
B2. The ability to interact effectively with others in order to work towards a common outcome.	4, 9
B3. The ability to select and use the appropriate level, style and means of communication.	9
B4. The ability to engage effectively and appropriately with information and communication technologies.	5, 8, 9
B5. The ability to practise as part of an interdisciplinary team.	5, 8
*C. INDEPENDENCE AND CREATIVITY*
C2. The ability to work and learn independently and effectively.	1, 6, 7, 10
C3. The ability to generate ideas and adapt innovatively to changing environments.	2, 4, 5, 6, 8, 9
C5. The ability to formulate and investigate problems, create solutions, innovate and improve current practices.	3, 4, 6, 7
C6. The abilities and skills that provide a foundation for future leadership roles.	6, 7
*D. CRITICAL JUDGEMENT*
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.	1, 2, 3, 4, 5, 6, 7, 8, 9
D4. The ability to process material and to critically analyse and integrate information from a wide range of sources.	6
D5. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions using an evidence-based approach.	6
*E. ETHICAL AND SOCIAL UNDERSTANDING*
E1. An understanding of social and civic responsibility.	8, 9
E3. An appreciation of the philosophical and social contexts of a discipline.	2, 8
E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study.	9
E5. A knowledge of other cultures and times and an appreciation of cultural diversity.	9
E7. The ability to work effectively and sensitively across all areas of society.	9
E8. An understanding of and respect for the roles and expertise of associated disciplines.	8, 9

Additional Course Information on Graduate Attributes

Students are expected to be able to write professional research reports in their semester-long assignment reports.

3. Learning Resources

3.1 Required Resources

The required text is: 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 books, but may find them useful. Copies of some of these books are available in the library. 1. Data Mining Concepts and Techniques, by Jiaweihan, Micheline Kamber, Morgan Kaufmann Publishers, 2001

2. Data Mining a Tutorial-Based Primer, by Richard J. Roiger and Michael W. Geatz, Addison Wesley, 2003

3. 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=INFS7203).

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). Students enrolled at the Ipswich campus will either be provided with a hard copy or given directions in class on how to obtain a free copy.

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
21 Jul 08 10:00 - 21 Jul 08 11:50	Introduction to Data Mining and Data Pre-processin (Lecture): Readings/Ref: Lecture Notes ; Required Text (Chapter 1-3);	3, 5, 7, 9, 10
28 Jul 08 10:00 - 28 Jul 08 11:50	Association Rules Mining (Lecture): Readings/Ref: Lecture Notes ; Required Text ; Reference Texts	1, 7, 8, 10
4 Aug 08 10:00 - 4 Aug 08 11:50	Classification (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	1, 3, 7, 8, 9, 10
11 Aug 08 10:00 - 11 Aug 08 11:50	Clustering (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	1, 7, 9, 10
18 Aug 08 10:00 - 18 Aug 08 11:50	Time Series and Sequential Data Mining (Lecture): Readings/Ref: Lecture Notes ; Required Text ; Reference Texts	1, 2, 6, 9, 10
25 Aug 08 10:00 - 5 Sep 08 11:59	Week 1-5 Topics (Self Directed Learning): These two weeks are used to read the materials that are related to the course assignment and middle semester examination. Readings/Ref: Lecture Notes ; Required Text ; Reference Texts ; Reference Texts ; Reference Texts	1, 2, 3, 5, 6, 7, 8, 9, 10
8 Sep 08 10:00 - 8 Sep 08 11:50	Middle Semester Exam (Progressive Exam): 1:30 Hrs Middle Semester Examp to be held during the lecture time. Readings/Ref: Lecture Notes ; Required Text ;	1, 2, 3, 5, 6, 7, 8, 9, 10
15 Sep 08 10:00 - 15 Sep 08 11:50	Text Mining (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	3, 4, 8, 9, 10
29 Sep 08 10:00 - 29 Sep 08 11:50	Web Content Mining (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	3, 4
6 Oct 08 10:00 - 6 Oct 08 11:50	Web Usage Mining (Lecture):	4, 5
13 Oct 08 10:00 - 13 Oct 08 11:50	Web Structure Mining (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	3, 4
20 Oct 08 10:00 - 20 Oct 08 11:50	Course Revision (Lecture): Readings/Ref: Lecture Notes ; Required Text ;	1, 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 assignment is designed to be a problem-solving writing assignment.

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
Paper Assignment (Individual)	21 Jul 08 10:00 - 17 Oct 08 23:59 Research paper-like project	20%	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Exam - Mid Semester During Class Middle Semester Exam	8 Sep 08 10:00 - 8 Sep 08 11:40 Non-programmable calculator is required.	20%	1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Exam - during Exam Period (School) Final Examination	Examination Period	60%	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: < 20

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: < 45

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: <50

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 & < = 84

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: >=85%

Other Requirements & Comments : Master students' assignment (i.e., 20% project) will have a different assessment scheme from that of the undergraduate students. The assessment scheme will have a higher requiremet and focus more on the analytical skills.

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.

5.4 Other Assessment Information

The assessment results (except the results of the final examination) will be posted on the course web site. Students will need to log in the system to view their own results.

5.5 Assessment Detail

Assignment (Individual)

Type: Paper
Learning Objectives Assessed: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Due Date:
21 Jul 08 10:00 - 17 Oct 08 23:59 Research paper-like project
Weight: 20%
Task Description: A Reseach Report on Data Mining Algorithms.
Criteria & Marking: The format of this assignment is a research-like report on a given data mining problem. There will be at least 4 sections: Introduction, Related Work, Solution/Methodology, and Evaluation/Conclution. The allocation of marks will be given in another detailed marking scheme on the course web site.
Submission: Assignment report (individual) will be submitted electronically (online submission).

Middle Semester Exam

Type: Exam - Mid Semester During Class
Learning Objectives Assessed: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Due Date:
8 Sep 08 10:00 - 8 Sep 08 11:40 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 1-5 (inclusive) weekly topics. The questions are of the similar style of the tutorial questions.
Criteria & Marking:

The marking is based on the correctness of the answers. For short discussion questions the answer will be marked according to the points that are associated to the concept and the insight that are in the answer. For computational questions the marking is based on the correctness of the development of the solutions. For the descriptive questions, the marking will be towards the correctness of the expressed meaning of concepts, ideas, methodology, procedure, and compare and contrast of the different ideas.

Weekly tutorial questions give a good indication on what is required in this examination.

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.

Criteria & Marking:

The marking of final examination is based on the same criteria of the middle semester examination. There will be questions about short discussions, definition of concepts, description of methodologies and algorithms, and the computational problem-driven questions.

The final grade is calculated based on the simple accumulation of the total marks in all assessment components.

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)

Feedback in this Course

Students are encouraged to use course newsgroup (uq.itee.infs4203) to post their questions. The lecturer will regularly check the newsgroup and provide the feedbacks to students.

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 examinations is set out in the University's Examinations policy (HUPP 3.30.5), sections 8 and 10.2.

The way in which late arrival at a School-controlled examination is dealt with will be at the discretion of the course coordinator, who may be guided by the policy for centrally controlled exams.

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 3.30.5. Unless otherwise indicated in the Course Profile, applications must be made in writing to the Head of School no later than one week 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; Room 218, Building 1, Ipswich) 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.

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