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FIT5045 Knowledge discovery and data mining - Semester 2, 2014

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

Gippsland (Off-campus)

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.) Study schedule for off-campus students:

  • Off-campus students generally do not attend lecture and tutorial sessions, however should plan to spend equivalent time working through the relevant resources and participating in discussion groups each week.

(c.) 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

Prohibitions

CSE5230, FIT5024

Prerequisites

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

Chief Examiner

Campus Lecturer

Gippsland

Dr Mortuza Ali

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

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.

If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp

Academic Overview

Learning Outcomes

At the completion of this unit students will:
  • 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 pre-process 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

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.

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.

Assessment Summary

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

Assessment Requirements

Assessment Policy

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Assignment 1: Analysis of Case Studies
    Description:
    Students are to answer questions in relation to case studies provided.
    Weighting:
    20%
    Criteria for assessment:

    Correctness in answering the questions.

    Due date:
    12 September 2014
  • Assessment task 2
    Title:
    Assignment 2
    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.

    Due date:
    10 October 2014

Examinations

  • Examination 1
    Weighting:
    60%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    None

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
  • Solutions to tutes, labs and assignments

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.

Required Resources

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.

Prescribed text(s)

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.

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

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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