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
Students will be expected to spend a total of 12 hours per week during semester on this unit as follows:
For on-campus students:
Lectures: 2 hours per week
Tutorials/Lab Sessions: 2 hours per week per tutorial
and up to an additional 8 hours in some weeks for completing lab and project work, private study and revision.
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
Neil Manson
Examination (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1 | 20% | 4 April 2012 |
| Assignment 2 | 20% | 2 May 2012 |
| 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
Please check with your lecturer before purchasing any Required Resources. Prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
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 | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | The Need for Data Mining | Practical work and Review Questions |
| 2 | Model Building | Practical work and Review Questions |
| 3 | Model Representation | Practical work and Review Questions |
| 4 | Data Mining Process | Review Questions |
| 5 | Performance Evaluation | Review Questions |
| 6 | Engineering the input and output | Practical work and Review Questions; Assignment 1 due 4 April 2012 |
| 7 | Algorithms | Practical work and Review Questions |
| 8 | Implementation Issues | Review Questions |
| 9 | Market basket analysis | Practical work and Review Questions; Assignment 2 due 2 May 2012 |
| 10 | Cluster Analysis | Review Questions |
| 11 | Anomaly Detection | Review Questions |
| 12 | Case Studies and Data Mining Applications | Review Questions |
| SWOT VAC | No formal assessment is undertaken SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.
Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)
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.
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz).
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. For Sunway see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/
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. At Sunway, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.
Academic support services may be available for students who have a disability or medical condition. Registration with the Disability Liaison Unit is required. Further information is available as follows:
Textbook:
Witten, I.H., Frank, E. & Hall, M.A.. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Third Edition, 2011.
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.