Modern methods of discovering patterns in large-scale databases are introduced, including classification, clustering and association rules analysis. These are contrasted with more traditional methods of finding information from data, such as data queries. Data pre-processing methods for dealing with noisy and missing data and with dimensionality reduction are reviewed. Hands-on case studies in building data mining models are performed using a popular software package.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Study schedule for off-campus students:
(c.) Additional requirements (all students):
CSE5230, FIT5024
Sound fundamental knowledge in maths and statistics. Basic database and computer programming knowledge.
Dr Mortuza Ali
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 the Student Evaluation of Teaching and Units (SETU) survey. 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, see:
www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
This unit was offered for the first time in Semester 2, 2009. The student reviews were good, but the unit will continually undergo improvements to ensure continual provision and delivery of up-to-date quality material.
Students will be requested to provide periodic informal anonymous feedback on the unit in Week 4 and Week 8.
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction to Data Science | |
| 2 | Model Building | |
| 3 | Model Evaluation | |
| 4 | Data Mining Process | |
| 5 | Data Preprocessing | |
| 6 | Classification | |
| 7 | Clustering | Assignment 1 due 12 September 2014 |
| 8 | Anomaly Detection and Unit Test | |
| 9 | Association Rules | |
| 10 | Web Mining | Assignment 2 due 10 October 2014 |
| 11 | Bayesian Data Mining | |
| 12 | Data Visualisation | |
| SWOT VAC | No formal assessment is undertaken in 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 learning system.
Examination (3 hours): 60%; In-semester assessment: 40%
| Assessment Task | Value | Due Date |
|---|---|---|
| Assignment 1: Analysis of Case Studies | 20% | 12 September 2014 |
| Assignment 2 | 20% | 10 October 2014 |
| Examination 1 | 60% | To be advised |
Faculty Policy - Unit Assessment Hurdles (http://intranet.monash.edu.au/infotech/resources/staff/edgov/policies/assessment-examinations/assessment-hurdles.html)
Academic Integrity - Please see resources and tutorials at http://www.monash.edu/library/skills/resources/tutorials/academic-integrity/
Correctness in answering the questions.
Students will be assessed on:
Further assessment criteria and marking sheet will be made available on the unit Moodle site.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html
Faculty of Information Technology Style Guide
Examination/other end-of-semester assessment feedback may take the form of feedback classes, provision of sample answers or other group feedback after official results have been published. Please check with your lecturer on the feedback provided and take advantage of this prior to requesting individual consultations with staff. If your unit has an examination, you may request to view your examination script booklet, see http://intranet.monash.edu.au/infotech/resources/students/procedures/request-to-view-exam-scripts.html
Types of feedback you can expect to receive in this unit are:
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.monash.edu.au/exams/special-consideration.html
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-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). Please note that it is your responsibility to retain copies of your assessments.
If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.
Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.
Students are to download the latest version of the free Data Mining Software WEKA from http://www.cs.waikato.ac.nz/ml/weka/ to work on their assignment and the tutorial exercises on their personal computers. WEKA is installed in the student labs used for the tutorials for this unit.
NOTE: Prescribed texts are freely available as e-books from Monash Library at no extra cost.
Limited copies of prescribed texts are available for you to borrow in the library.
I.H. Witten and E. Frank. (2011). Data Mining: Practical Machine Learning Tools and Techniques. (3rd Edition) Morgan Kaufmann. This serves both as a textbook on data mining and as a manual for using the main data mining tool in this subject, Weka.
J. Han and M.Kamber. (2011). Data Mining Concepts and Techniques. (3rd Edition) Morgan Kaufmann.
R. Roiger and M. Geatz. (2003). Data Mining: A Tutorial-based Primer. () Pearson Education, Inc.
G. Gupta. (2006). Introduction to Data Mining and Case Studies. () Prentice-Hall, New Delhi.
P. Tan, M. Steinback, V. Kumar. (2006). Introduction to Data Mining. () Pearson Education, Inc.
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: www.policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/
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 http://www.monash.edu.au/students. For Malaysia 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, resources and programs that enable you to save time and be more effective in your learning and research. Go to www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Malaysia, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.