Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases. The new data mining subject fits in well within the Bachelor of Computing course offered at Gippsland. This subject is to serve as an advanced study for students who have completed GCO2815 (Database Management System) and would like to study how to apply the new techniques to unlock the information and knowledge in large databases. The "applications of data mining" subject offers students an opportunity to study this fast growing field: "80% of the Fortune 500 companies are currently involved in a data mining pilot project or have already deployed one or more data mining production systems." (E. Simoudis, foreword in Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. & Zanasi, A., Discovering Data Mining: from concept to implementation, Prentice Hall, 1997.). This unit will offer and equip students with interdisciplinary skills that will be in use in many corporations when they graduate.

At the completion of this unit, students will have:

Knowledge of:

1. the motivation and the need for data mining
2. characteristics of major components of the data mining process
3. the basic principles of methods and operations for data mining
4. case studies to bridge the connection between hands-on experience and real-world applications
5. key and emerging application areas

Understanding of current major research issues

Skills in using data mining tools to solve data mining problems

This unit will provide students with the motivation and the need for data mining, an understanding of the major components of the data mining process, the basic principles of various methods and operations for data mining, knowledge of key and emerging application areas, case studies to bridge the connection between hands-on experience and real-world applications, practical skills in solving data mining problems, and expose students to the current major research issues in this area.

Prescribed Texts:

Recommended Readings:

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. & Linoff G., Data Mining Techniques: For Marketing, Sales, and Customer Support; John Wiley & Sons, Inc.; 1997.
4. Westphal, C. & Blaxton, T., Data Mining Solutions, John Wiley & Sons, 1998.
5. Weiss, S.M. & Indurkhya, N., Predictive Data Mining, Morgan Kaufmann, 1997.
6. Smith K., Introduction to Neural Networks and Data Mining for Business Applications. Eruditions Publishing, 1999.
7. Thuraisingham, Bhavani M, Data mining : technologies, techniques, tools, and trends; Boca Raton : CRC Press, c1999.}

Assignments 40% 3-hour examination 60%

The assessment methods in this subject are by: Assignments worth 40% of the total subject assessment. The assignments will assess students' ability to apply their knowledge and problem solving skill appropriately to different data mining problems. A 3-hour examination worth 60% of the total subject assessment. The examination assess students' understanding of (i) key concepts such as model building, model evaluation and selection, (ii) the connection with closely related fields such as traditional data analysis and data warehousing, and (iii) current research areas such as text mining and web analytics.

• Software Title: WEKA
• Vendor:
• Version: 3.4.3
• OS: MS Windows XP (X) and/or LINUX ( )
• License Details: (A valid software license certificate/s or Vendor permission must be available upon request if the software is to be installed for teaching)
• Are we currently licensed: Yes ( ) or No ( ) (If unsure, please check with your local IT support )
• If (Yes), who is the contact person for this software?:
• If (No), will the software license be arranged by: Staff member requesting the software ( ) or Technical Services* ( ) ? *If the latter is selected, please discuss your requirement/s with myself ASAP, prior to finalising your request, as there could be financial or resourcing implications.
• Is the software to be installed and accessed from a: Client/Workstation ( ), or Server ( ) ?
• Type of license: Site License ( ), Freeware ( ), FOSS (Free and Open Source Software) (X), Shareware ( ), Lab based ( ) # of Licenses needed ____
• In what week will you commence using the software in class? Semester 1 (Week 1 ) and/or Semester 2 (Week ___ ):
• Nominated Staff For Acceptance Testing Software: (Name) (Phone) (Email)
• Additional Information: Special Requirements, Software Configuration/Installation Requirements, etc: software can be downloaded from http://www.cs.waikato.ac.nz/~ml/weka/

• Software Title: Magnum Opus
• Vendor:
• Version: 3.0
• OS: MS Windows XP (X) and/or LINUX ( )
• In what week will you commence using the software in class? Semester 1 (Week 1 ) and/or Semester 2 (Week ___ ):
  • Type of license: Site License ( ), Freeware (X), FOSS (Free and Open Source Software) ( ), Shareware ( ), Lab based ( ) # of Licenses needed ____
• Additional Information: Special Requirements, Software Configuration/Installation Requirements, etc: software can be downloaded from http://www.rulequest.com/