FIT5045 Knowledge discovery and data mining - Semester 2, 2013

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.

Mode of Delivery

Contact Hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Workload requirements

Students will be expected to spend a total of 12 hours per week during semester on this unit as follows:

For on-campus students:

• two-hour lecture and
• two-hour tutorial (or laboratory) (requiring advance preparation)
• a minimum of 2 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations.

You will need to allocate up to 5 hours per week in some weeks, for use of a computer, including time for newsgroups/discussion groups.

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.

Unit Relationships

Prohibitions

CSE5230, FIT5024

Prerequisites

Sound fundamental knowledge in maths and statistics. Basic database and computer programming knowledge.

Chief Examiner

Campus Lecturer

Academic Overview

Learning Outcomes

• be able to differentiate between supervised and unsupervised learning;
• know how to apply the main techniques for supervised and unsupervised learning;
• know how to use statistical methods for evaluating data mining models;
• be able to perform data pre-processing for data with outliers, incomplete and noisy data;
• be able to extract and analyse patterns from data using a data mining tool;
• have an understanding of the difference between discovery of hidden patterns and simple query extractions in a dataset;
• have an understanding of the different methods available to facilitate discovery of hidden patterns in a dataset;
• have developed the ability to preprocess data in preparation for data mining experiments;
• have developed the ability to evaluate the quality of data mining models;
• be able to appreciate the need to have representative sample input data to enable learning of patterns embedded in population data;
• be able to appreciate the need to provide quality input data to produce useful data mining models;
• have acquired the skill to use the common features in data mining tools;
• have acquired the skill to use the visualisation features in a data mining tools to facilitate knowledge discovery from a data set;
• have acquired the skill to compare data mining models based on the results on a set of performance criteria;
• be able to work in a team to extract knowledge from a common data set using different data mining methods and techniques.

Unit Schedule

*Unit Schedule details will be maintained and communicated to you via your learning system.

Assessment Summary

Examination (3 hours): 60%; In-semester assessment: 40%

Teaching Approach

Assessment Requirements

Assessment Policy

Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)

Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/

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:

  Correctness in answering the questions.

  Due date:

  17 September 2013

• Assessment task 2

  Title:

  Assignment

  Description:

  This assignment requires students to use the data mining tool, WEKA, to explore several models and then choose one that will likely to produce the best models for a given data set.

  Weighting:

  20%

  Criteria for assessment:

  Students will be assessed on:

  • The degree to which the submission meet the assignment specification
  • The quality of the data preprocessing and the design of experiments
  • How well the experiments are conducted and summarised
  • How well the results of the experiments are analysed and documented

  Further assessment criteria and marking sheet will be made available on the unit Moodle site.

  Members in each group will receive the same marks. If there are issues/concerns about individual contributions within a group, a peer evaluation form will be used.

  Due date:

  18 October 2013

Examinations

• Examination 1

  Weighting:

  60%

  Length:

  3 hours

  Type (open/closed book):

  Closed book

  Electronic devices allowed in the exam:

  None

Learning resources

Feedback to you

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

Extensions and penalties

Returning assignments

Assignment submission

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.

Required Resources

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.

Prescribed text(s)

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.

Recommended text(s)

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.

Other Information

Policies

Graduate Attributes Policy

Student services

Monash University Library

Disability Liaison Unit

Your feedback to Us

Previous Student Evaluations of this Unit

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.  In Week 11 the Monquest and in Week 13 the Unit Evaluations will be conducted.