In the modern corporate world, data is viewed not only as a necessity for day-to-day operation, it is seen as a critical asset for decision making. However, raw data is of low value. Succinct generalisations are required before data gains high value. Data mining produces knowledge from data, making feasible sophisticated data-driven decision making. This unit will provide students with an understanding of the major components of the data mining process, the various methods and operations for data mining, knowledge of the applications and technical aspects of data mining, and an understanding of the major research issues in this area.
2 hrs lectures/wk, 2 hrs laboratories/wk
For on campus students, workload commitments are:
You will need to allocate up to 5 hours per week in some weeks, for use of a computer, including time for newsgroups/discussion groups.
Off-campus students generally do not attend lecture and tutorial sessions, however, you should plan to spend equivalent time working through the relevant resources and participating in discussion groups each week.
CSE3212, GCO3828
FIT1004 or FIT2010 or equivalent
Kai Ming Ting
Kai Ming Ting
Sakkie van Zyl
Elsa Phung
At the completion of this unit students will have -
A knowledge and understanding of:
Examination (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 | 20% | 6 April 2011 |
| Assignment 2 | 20% | 4 May 2011 |
| Examination 1 | 60% | To be advised |
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
For more information on Monash's educational strategy, and on student evaluations, see:
http://www.monash.edu.au/about/monash-directions/directions.html
http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
1. Software Title: WEKA, version 3.6
2. Magnum OPUS version 4
Both are freeware from the websites stated in the relevant practical web pages.
| Week | Date* | Activities | Assessment |
|---|---|---|---|
| 0 | 21/02/11 | No formal assessment or activities are undertaken in week 0 | |
| 1 | 28/02/11 | The Need for Data Mining | Practical work and Review Questions |
| 2 | 07/03/11 | Model Building | Practical work and Review Questions |
| 3 | 14/03/11 | Model Representation | Practical work and Review Questions |
| 4 | 21/03/11 | Data Mining Process | Review Questions |
| 5 | 28/03/11 | Performance Evaluation | Review Questions |
| 6 | 04/04/11 | Engineering the input and output | Practical work and Review Questions; Assignment 1 due 6 April 2011 |
| 7 | 11/04/11 | Algorithms | Practical work and Review Questions |
| 8 | 18/04/11 | Implementation Issues | Review Questions |
| Mid semester break | |||
| 9 | 02/05/11 | Market basket analysis | Practical work and Review Questions; Assignment 2 due 4 May 2011 |
| 10 | 09/05/11 | Cluster Analysis | Review Questions |
| 11 | 16/05/11 | Anomaly Detection | Review Questions |
| 12 | 23/05/11 | Case Studies and Data Mining Applications | Review Questions |
| 30/05/11 | SWOT VAC | No formal assessment is undertaken SWOT VAC | |
*Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.
To pass a unit which includes an examination as part of the assessment a student must obtain:
If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit
Assignment tasks are required to be completed by students in pairs.
To get a Pass grade, students must perform data preparation/preprocessing, produce several different models and choose the best model, and submit a clearly written report describing the process.
To get a better grade, students must show that they have performed extra data analysis and preprocessing, explored a wide range of different models and describe how the final model is produced and how it can be applied for future predictions.
More detailed criteria will be provided in the sample marksheet on the assignment web page.
More detailed criteria will be provided in the sample marksheet on the assignment web page.
Assignment coversheets are available via
"Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
You MUST submit a completed coversheet with all assignments, ensuring
that the plagiarism declaration section is signed.
Submission must be made by the due date otherwise penalties will be enforced.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.infotech.monash.edu.au/resources/student/equity/special-consideration.html.
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them.
You can find Monash's Education Policies at:
http://policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis
Reading List
Textbook:
Witten, I.H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Second Edition, 2005.
References:
1. Kennedy, R.L., Lee, Y. Roy, B.V., Reed, C.D. & Lippman, R.P., Solving Data Mining Problems through Pattern Recognition, Prentice Hall, 1998.
2. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. & Zanasi, A., Discovering Data Mining: from concept to implementation, Prentice Hall, 1997.
3. Berry, J.A.M. & Linoff, G. Data Mining Techniques for Marketing, Sales, and Customer Support, John Wiley & Sons, 1997.
4. Tan, P-N, Steinbach, M. & Kumar, V. Introduction to Data Mining, Addison Wesley, 2006.
5. Han, J. & Kamber, M. Data Mining: Concepts and Techniques, Morgan Kaufmann, Second Edition, 2006.
6. Dunham, M.H., Data Mining: Introductory and Advance Topics, Pearson Education, 2003.
7. Groth, R., Data Mining: Building competitive advantage, Prentice Hall, 2000.
8. Berson,. A., Smith, S. & Thearling, K., Building Data Mining Applications for CRM, McGraw Hill. 2000.
9. Berry, J.A.M. & Linoff, G. Mastering Data Mining: The Art and Science of Customer Relationship Management, John Wiley & Sons, 2000.
10. Mena, J. Data Mining Your Website. Digital Press, 1999.
11. Westphal, C. & Blaxton, T. Data Mining Solutions, John Wiley & Sons, 1998.
12. Quinlan, J.R. C4.5: Program for Machine Learning, Morgan Kaufmann, 1993.
13. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, 1996.