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GCO5828 Applications of Data Mining(DISESTABLISHED FB 05/07)

Chief Examiner

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Unit Code, Name, Abbreviation

GCO5828 Applications of Data Mining(DISESTABLISHED FB 05/07) (14 Dec 2007, 09:34am) [Data Mining]

Reasons for Introduction

Obsolete Reasons for Introduction

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.Reasons for this changeGSCIT proposes to change the teaching mode of this unit from off-campus learning only to off-campus and on-campus learning. The purpose of this change is to satisfy the ESOS on-campus offering requirement for international students, in particular for on-campus honours and MIT (minor thesis) programs.MIT (minor thesis) was recently introduced and has become a preferred replacement for Honours by some international students. GSCIT has about 3 to 5 international Honours and MIT(minor thesis) students studying in Gippsland campus every year. Since GSCIT only offers off-campus learning for the MIT program and the requirement of ESOS to have on-campus offerings for all on-campus courses, GSCIT is currently barred from enrolling any international students who wish to study on-campus into the MIT (minor thesis) program because of the ESOS requirement.The proposed change does not increase the lecture, staff or library requirement.This change also addresses the new requirement to enroll honours students for the individual on-campus units in Callista.

Objectives

Statement of Objectives (27 Mar 2006, 7:39pm)

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

Skills in using data mining tools to solve data mining problems

Understanding of current major research issues

Unit Content

Handbook Summary (27 Mar 2006, 7:39pm)

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.

Recommended Reading (27 Mar 2006, 7:40pm)

Prescribed Text:

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

Reference Texts:

  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. Recommended Readings: 1.Witten, I.H. and Frank, E., Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco, CA. 2000.
  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.

Teaching Methods

Assessment

Strategies of Assessment (27 Mar 2006, 7:41pm)

Assignments 40% 3-hour examination 60%

Assessment Relationship to Objectives (27 Mar 2006, 7:43pm)

The assessment methods in this subject are by: Assignments worth 40% of the total subject assessment. The first two assignments will assess students' ability to apply their knowledge and problem solving skill appropriately to different data mining problems. A reading assignment assess students ability to apply their knowledge and use an appropriate tool available from the web (without guidance about the tool in the subject book) to solve a data mining problem and identify the research issues involved. 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.

Workloads

Resource Requirements

Software Requirements (27 Mar 2006, 7:43pm)

Prerequisites

Faculty Information

Proposer

Kai Ming Ting

Approvals

School: 14 Dec 2007 (Julianna Dawidowicz)
Faculty Education Committee: 14 Dec 2007 (Julianna Dawidowicz)
Faculty Board: 14 Dec 2007 (Julianna Dawidowicz)
ADT:
Faculty Manager:
Dean's Advisory Council:
Other:

Version History

08 Nov 2002 Kim Styles GSCIT proposes to change the teaching mode of this unit from off-campus learning only to off-campus and on-campus learning, to accomodate ESOS and Callista enrolment requirements for Honours and MIT(minor thesis) students. Minor modifications have been made to this version to prepare it for transmission to FEC agenda.
17 Oct 2005 David Sole Added Software requrirements template
21 Oct 2005 David Sole Updated requirements template to new format
27 Oct 2005 Kai Ting modified ResourceReqs/SoftwareReqs
27 Mar 2006 Kai Ting modified UnitObjectives/ObjText; modified Classification; modified UnitContent/HandbookSummary; modified UnitContent/RecommendedReading; modified Assessment/Strategies; modified Assessment/Objectives; modified ResourceReqs/SoftwareReqs
14 Dec 2007 Julianna Dawidowicz modified UnitName
14 Dec 2007 Julianna Dawidowicz GCO5828 Chief Examiner Approval, ( proxy school approval )
14 Dec 2007 Julianna Dawidowicz FEC Approval
14 Dec 2007 Julianna Dawidowicz FacultyBoard Approval - Faculty Board approved the disestablishment of this unit at 05/07 meeting

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