Advanced methods of discovering patterns in large-scale multi-dimensional databases are discussed. Solving classification, clustering, association rules analysis and regression problems on different kinds of data are covered. Data pre-processing methods for dealing with noisy and missing data in the context of Big Data are reviewed. Evaluation and analysis of data mining models are emphasised. Hands-on case studies in building data mining models are performed using popular modern software packages.
Minimum total expected workload equals 12 hours per week comprising:
(a.) Contact hours for on-campus students:
(b.) Additional requirements (all students):
FIT5047 or FIT5045 or equivalent
Sound fundamental knowledge in maths and statistics; database and computer programming knowledge.
Grace Rumantir
Consultation hours: Wednesday 2pm-4pm
Yuan Jin
Consultation hours: TBA
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| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction | There is a self-assessed test (not marked) on basic maths and statistics and the fundamentals of Data Mining on Moodle that will be discussed in the Week 1 tutorial. Please complete this to see if you need to do further study prior to completing this unit. |
| 2 | Data Preprocessing | |
| 3 | Data Warehousing and Data Mining | |
| 4 | Classification and Prediction | |
| 5 | Cluster Analysis | |
| 6 | Mining Stream, Time-Series and Sequential Data | |
| 7 | Graph Mining, Social Network Analysis and Multirelational Data Mining | |
| 8 | Unit Test (during the lecture timeslot, tutorials are still on) | Unit Test during Week 8 lecture (Thursday 18 September 2014) |
| 9 | Ensemble Methods in Data Mining | Assignment Stage 1 due start of Week 9 lecture (Thursday 25 September 2014) |
| 10 | Mining Object, Spatial, Multimedia, Text and Web Data (Part 1) | |
| 11 | Mining Object, Spatial, Multimedia, Text and Web Data (Part 2) | Assignment Stage 2 due start of Week 11 lecture (Thursday 16 October 2014) |
| 12 | Application & Trends in Data Mining and Revision | |
| 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 |
|---|---|---|
| Unit Test | 20% | Unit Test during Week 8 lecture (Thursday 18 September 2014) |
| Report on Advanced Topics in Data Mining | 20% | Assignment Stage 1 due start of Week 9 lecture (Thursday 25 September 2014). Assignment Stage 2 due start of Week 11 lecture (Thursday 16 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/
Correct answers to questions, and quality of solutions to problems, which demonstrates understanding of the learning materials. Further detail of the format and coverage of the unit test will be made available on Moodle.
The report will be assessed on the usual criteria, namely: breadth of literature survey, quality of analysis of literature and topicality.
There are 2 stages of the assignment:
Stage 1: Write up of the structure of the report and the aspects to be covered in the literature review (non-assessable)
Stage 2: Submission (20%).
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.
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Key educational policies include:
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