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):
See also Unit timetable information
FIT5047 or FIT5045 or equivalent
Sound fundamental knowledge in maths and statistics; database and computer programming knowledge.
Grace Rumantir
Consultation hours: Monday 4pm-5pm, Thursday 1-2pm or by appointment
Ashish Singh
Consultation hours: TBA
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.
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Based on previous student feedback this unit is well structured and no major changes have been made for this semester. Further refinements of the materials on the topics covered will be made on a weekly basis and made available on Moodle.
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 | 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 (Monday 14 September 2015) |
| 9 | Ensemble Methods in Data Mining | Assignment Stage 1 due online Monday 21 September 2015 at 11am |
| 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 on online Monday 12 October 2015 at 11am |
| 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 | 17% | Unit Test during Week 8 lecture (Monday 14 September 2015) |
| Report on Implementations of Advanced Data Mining Techniques | 17% | Assignment Stage 1 due online Monday 21 September 2015. Assignment Stage 2 due online Monday 12 October 2015) |
| 9 Weekly Revision Quizzes | 3% | Each weekly quiz will open at the end of the last tutorial in the week and will close at midnight on the Sunday of the week. |
| Participation in the clicker sessions in the lecture from Week 2 to Week 12 | 3% | Weekly (during the lectures from Week 2 to Week 12) |
| 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, topicality, the use of a data set to illustrate the implementations of the relevant advanced data mining methods.
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 report (non-assessable)
Stage 2: Submission (17%).
Each weekly online quiz will have 10 multiple choice questions. There is no negative mark for wrong answers. The mark of each quiz will be recorded based on student's submission of his/her quiz attempt prior to the closing date of each quiz.
Active participation in lecture sessions using the response systems is expected. 3% of the final marks is allocated for this participation. The mark will be allocated based on the percentage of recorded participation in the clicker sessions during lectures.
Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.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
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