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FIT1004 or FIT2010 or equivalent

Chief Examiner

Kai Ming Ting

Campus Lecturer


Kai Ming Ting

South Africa

Sakkie van Zyl


Elsa Phung

Learning Objectives

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% 6 April 2011
    Assignment 2 20% 4 May 2011
    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.


    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:

    Previous Student Evaluations of this unit

    If you wish to view how previous students rated this unit, please go to

    Required Resources

    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 Date* Activities Assessment
    0 21/02/11   No formal assessment or activities are undertaken in week 0
    1 28/02/11 The Need for Data Mining Practical work and Review Questions
    2 07/03/11 Model Building Practical work and Review Questions
    3 14/03/11 Model Representation Practical work and Review Questions
    4 21/03/11 Data Mining Process Review Questions
    5 28/03/11 Performance Evaluation Review Questions
    6 04/04/11 Engineering the input and output Practical work and Review Questions; Assignment 1 due 6 April 2011
    7 11/04/11 Algorithms Practical work and Review Questions
    8 18/04/11 Implementation Issues Review Questions
    Mid semester break
    9 02/05/11 Market basket analysis Practical work and Review Questions; Assignment 2 due 4 May 2011
    10 09/05/11 Cluster Analysis Review Questions
    11 16/05/11 Anomaly Detection Review Questions
    12 23/05/11 Case Studies and Data Mining Applications Review Questions
      30/05/11 SWOT VAC No formal assessment is undertaken SWOT VAC

    *Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.

    Assessment Policy

    To pass a unit which includes an examination as part of the assessment a student must obtain:

    • 40% or more in the unit's examination, and
    • 40% or more in the unit's total non-examination assessment, and
    • an overall unit mark of 50% or more.

    If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit

    Assessment Tasks


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

    • Assessment task 1
      Assignment 1
      This assignment requires students to use the data mining tool, WEKA, to build a good model from a given set of data, and write a report describing the data mining process.
      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:
      6 April 2011
    • Assessment task 2
      Assignment 2
      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.
      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:
      4 May 2011


    • Examination 1
      3 hours
      Type (open/closed book):
      Closed book
      Electronic devices allowed in the exam:

    Assignment submission

    Assignment coversheets are available via "Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
    You MUST submit a completed coversheet with all assignments, ensuring that the plagiarism declaration section is signed.

    Extensions and penalties

    Returning assignments


    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:

    Key educational policies include:

    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 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. 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

    Reading List


    Witten, I.H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Second Edition, 2005.


    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.

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