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Unit details

SIT717 - Advanced Data Mining

Note: You are seeing the 2011 view of this unit information. These details may no longer be current. [Go to the current version]
Enrolment modes:(B, X)
Credit point(s):1
Offerings: Trimester 2
Previously coded as:SCC717
EFTSL value: 0.125
Assumed Knowledge:Knowledge appropriate to the topic.
Unit chair:G Li
Contact hours:

1 x 1 hour lecture per week, 1 x 2 hour practical per week

Note:

Online teaching methods require internet access. Please refer to the most current computer specifications.

Content

The unit will begin with an introduction to the advanced data mining processes such as CRISP-DM,  then explain the requirements of business intelligence, in the context of customer relationship management.  Methods to be taught in this unit includes variants of association rule discovery (for basket analysis); prediction techniques such as inductive inference of decision trees and Bayes models (for market prediction), clustering techniques such as self-organization maps (for market segmentation), but with emphasis on real world applications.  A selection of recent real world business intelligence case studies will be incorporated in this unit to illustrate the introduced techniques.  Advanced topics such as privacy-preserving data mining, text and sentiment mining will also be covered.

Assessment

Projects (30%, 50%, 20%) 100%

Unit Fee Information

Student Contribution Rate*Student Contribution Rate**Student Contribution Rate***Fee rate - Domestic Students Fee rate - International students
$969$969$969$2493$2786

* Student contribution rate for Commonwealth Supported students who commenced studies from 2010
** Student contribution rate for Commonwealth Supported students who commenced studies from 2009
*** Student contribution rate for Commonwealth Supported students who commenced studies from 2008
**** Fee rate for students who commenced studies from 2005
***** Fee rate for students who commenced studies from 2006
Please note: Unit fees listed do not apply to Deakin Prime students.

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