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FIT5142 Advanced data mining - Semester 2, 2014

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.

Mode of Delivery

Caulfield (Day)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 2-hour laboratory

(b.) Additional requirements (all students):

  • A minimum of 8 hours independent study per week for completing lab and project work, private study and revision.

Unit Relationships

Prerequisites

FIT5047 or FIT5045 or equivalent
Sound fundamental knowledge in maths and statistics; database and computer programming knowledge.

Chief Examiner

Campus Lecturer

Caulfield

Grace Rumantir

Consultation hours: Wednesday 2pm-4pm

Tutors

Caulfield

Yuan Jin

Consultation hours: TBA

Your feedback to Us

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

Academic Overview

Learning Outcomes

On successful completion of this unit, students should be able to:
  • explain the kinds of data from which knowledge can be mined, the way each data type can be presented to a data mining algorithm, the kinds of patterns that can be mined from each data type;
  • evaluate the quality of data mining models;
  • perform pre-processing of large-scale multi-dimensional datasets in preparation for data mining experiments;
  • perform data pre-processing for data with outliers, incomplete and noisy data;
  • compare the various learning algorithms and the ability to effectively apply suitable algorithms to mine frequent patterns and associations from data, to perform data classification, data clustering and regression analysis;
  • use modern data mining tools to solve non-trivial data mining problems;
  • research the current trends in data mining applications;
  • work in a team to extract knowledge from a common dataset using various data mining methods and techniques.

Unit Schedule

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.

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach helps students to initially encounter information at lectures, discuss and explore the information during tutorials, and practice in a hands-on lab environment.

Assessment Summary

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

Assessment Requirements

Assessment Policy

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Unit Test
    Description:
    Closed-book unit test to be conducted in the lecture time slot in Week 8.
    Weighting:
    20%
    Criteria for assessment:

    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.

    Due date:
    Unit Test during Week 8 lecture (Thursday 18 September 2014)
  • Assessment task 2
    Title:
    Report on Advanced Topics in Data Mining
    Description:
    A 3,000 word essay on an approved research topic on advances in Data Mining.
    Weighting:
    20%
    Criteria for assessment:

    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%).

    Due date:
    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)

Examinations

  • Examination 1
    Weighting:
    60%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    Scientific Calculators

Learning resources

Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html

Faculty of Information Technology Style Guide

Feedback to you

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:

  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Test results and feedback
  • Quiz results

Extensions and penalties

Returning assignments

Assignment submission

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.

Online submission

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.

Other Information

Policies

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:

Faculty resources and policies

Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/

Graduate Attributes Policy

Student Charter

Student services

Monash University Library

Disability Liaison Unit

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.

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