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FIT3002 Applications of data mining - Semester 1, 2012

In the modern corporate world, data is viewed not only as a necessity for day-to-day operation, it is seen as a critical asset for decision making. However, raw data is of low value. Succinct generalisations are required before data gains high value. Data mining produces knowledge from data, making feasible sophisticated data-driven decision making. This unit will provide students with an understanding of the major components of the data mining process, the various methods and operations for data mining, knowledge of the applications and technical aspects of data mining, and an understanding of the major research issues in this area.

Mode of Delivery

  • Gippsland (Day)
  • Gippsland (Off-campus)
  • South Africa (Day)

Contact Hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Workload

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

For on-campus students:
Lectures: 2 hours per week
Tutorials/Lab Sessions: 2 hours per week per tutorial
and up to an additional 8 hours in some weeks for completing lab and project work, private study and revision.

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

CSE3212, GCO3828

Prerequisites

FIT1004 or FIT2010 or equivalent

Chief Examiner

Campus Lecturer

Gippsland

Kai Ming Ting

South Africa

Neil Manson

Academic Overview

Outcomes

At the completion of this unit students will have -
A knowledge and understanding of:
  • the motivation and the need for data mining;
  • characteristics of major components of the data mining process;
  • the basic principles of methods and operations for data mining;
  • case studies to bridge the connection between hands-on experience and real-world applications;
  • key and emerging application areas;
  • current major research issues.
Developed the skills to:
  • use data mining tools to solve data mining problems.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

Assessment Summary

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

Assessment Task Value Due Date
Assignment 1 20% 4 April 2012
Assignment 2 20% 2 May 2012
Examination 1 60% To be advised

Teaching Approach

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

Feedback

Our feedback to You

Types of feedback you can expect to receive in this unit are:
  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Other: Solutions to review questions and assignments

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 SETU, Student Evaluation of Teacher and Unit. 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, and on student evaluations, see:
http://www.monash.edu.au/about/monash-directions/directions.html
http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

Previous Student Evaluations of this unit

A topic on "cluster analysis and anomaly detection" and additional reading on application have been added in 2008 to broaden students' knowledge in this area.

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

Required Resources

Please check with your lecturer before purchasing any Required Resources. Prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.

1. Software Title: WEKA, version 3.6
2. Magnum OPUS version 4
Both are freeware from the websites stated in the relevant practical web pages.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 The Need for Data Mining Practical work and Review Questions
2 Model Building Practical work and Review Questions
3 Model Representation Practical work and Review Questions
4 Data Mining Process Review Questions
5 Performance Evaluation Review Questions
6 Engineering the input and output Practical work and Review Questions; Assignment 1 due 4 April 2012
7 Algorithms Practical work and Review Questions
8 Implementation Issues Review Questions
9 Market basket analysis Practical work and Review Questions; Assignment 2 due 2 May 2012
10 Cluster Analysis Review Questions
11 Anomaly Detection Review Questions
12 Case Studies and Data Mining Applications Review Questions
  SWOT VAC No formal assessment is undertaken 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 MUSO (Blackboard or Moodle) learning system.

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)

Assessment Tasks

Participation

Assignment tasks are required to be completed by students in pairs.

  • Assessment task 1
    Title:
    Assignment 1
    Description:
    This assignment requires students to use the data mining tool, WEKA, to build a good performing model from a given set of data, and write a report describing the data mining process.
    Weighting:
    20%
    Criteria for assessment:

    To get a Pass grade, students must perform data preparation/preprocessing, produce several different models and choose the best model, and submit a clearly written report describing the process.
    To get a better grade, students must show that they have performed extra data analysis and preprocessing, explored a wide range of different models and describe how the final model is produced and how it can be applied for future predictions.

    More detailed criteria will be provided in the sample marksheet on the assignment web page.

    Due date:
    4 April 2012
  • 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 be likely to produce the largest profit within the budgetary constraint for a mass mailing campaign. Students are required to write a report to describe the process and analysis involved.
    Weighting:
    20%
    Criteria for assessment:
    • Must have a clear problem definition section that defines the inputs (and their types: nominal or numeric) and output; evaluation method and performance measure used (train and test using the given data sets and choose model based on profit).
    • Produce several different models.
    • Choose the best model which maximises profit in all parts of the process.
    • A clearly written report which shows the high level process taken.

    More detailed criteria will be provided in the sample marksheet on the assignment web page.

    Due date:
    2 May 2012

Examinations

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

Assignment submission

It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-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).

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the VLE site for this unit, which you can access via links in the my.monash portal.

Extensions and penalties

Returning assignments

Other Information

Policies

Student services

The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students. For Sunway see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/

The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. At Sunway, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.

Academic support services may be available for students who have a disability or medical condition. Registration with the Disability Liaison Unit is required. Further information is available as follows:

  • Website: http://monash.edu/equity-diversity/disability/index.html;
  • Email: dlu@monash.edu
  • Drop In: Equity and Diversity Centre, Level 1 Gallery Building (Building 55), Monash University, Clayton Campus, or Student Community Services Department, Level 2, Building 2, Monash University, Sunway Campus
  • Telephone: 03 9905 5704, or contact the Student Advisor, Student Commuity Services at 03 55146018 at Sunway

Reading list

Textbook:

Witten, I.H., Frank, E. & Hall, M.A.. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Third Edition, 2011.

References:

1. Kennedy, R.L., Lee, Y. Roy, B.V., Reed, C.D. & Lippman, R.P., Solving Data Mining Problems through Pattern Recognition, Prentice Hall, 1998.

2. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. & Zanasi, A., Discovering Data Mining: from concept to implementation, Prentice Hall, 1997.

3. Berry, J.A.M. & Linoff, G. Data Mining Techniques for Marketing, Sales, and Customer Support, John Wiley & Sons, 1997.

4. Tan, P-N, Steinbach, M. & Kumar, V. Introduction to Data Mining, Addison Wesley, 2006.

5. Han, J. & Kamber, M. Data Mining: Concepts and Techniques, Morgan Kaufmann, Second Edition, 2006.

6. Dunham, M.H., Data Mining: Introductory and Advance Topics, Pearson Education, 2003.

7. Groth, R., Data Mining: Building competitive advantage, Prentice Hall, 2000.

8. Berson,. A., Smith, S. & Thearling, K., Building Data Mining Applications for CRM, McGraw Hill. 2000.

9. Berry, J.A.M. & Linoff, G. Mastering Data Mining: The Art and Science of Customer Relationship Management, John Wiley & Sons, 2000.

10. Mena, J. Data Mining Your Website. Digital Press, 1999.

11. Westphal, C. & Blaxton, T. Data Mining Solutions, John Wiley & Sons, 1998.

12. Quinlan, J.R. C4.5: Program for Machine Learning, Morgan Kaufmann, 1993.

13. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy, R. Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, 1996.