DATA MINING
CSE5DMI
2021
Credit points: 15
Subject outline
Data Mining refers to various techniques which can be used to uncover hidden information from a database. The data to be mined may be complex data including big data, multimedia, spatial and temporal data, biological and health data. Data Mining has evolved from several areas including: databases, artificial intelligence, algorithms, information retrieval and statistics. This subject is designed to provide you with a solid understanding of data mining concepts and tools. The subject covers algorithms and techniques for data pre-processing, data classification, association rule mining, and data clustering. The subject also covers domain applications where data mining techniques are used.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorLydia Cui
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveYes
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
PrerequisitesCSE4OOF OR CSE5CES OR CSE4IP OR CSE1OOF
CSE1OOF or CSE4OOF or CSE5CES OR CSE4IP. Admission into SMBB or equivalent (discuss with subject coordinator)
Co-requisitesN/A
Incompatible subjectsCSE4DMI
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsN/A
Minimum credit point requirementN/A
Assumed knowledgeN/A
Learning resources
Introduction to Data Mining
Resource TypeBook
Resource RequirementRecommended
AuthorTan, PN, Steinback, M & Kumar, V
Year2006
Edition/VolumeN/A
PublisherMORGAN KAUFMANN
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Data Mining: Concepts and Techniques
Resource TypeBook
Resource RequirementRecommended
AuthorJiawei Han, Micheline Kamber and Jian Pei
Year2011
Edition/VolumeN/A
PublisherMorgan Kaufmann
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Career Ready
Career-focusedNo
Work-based learningNo
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Graduate Capabilities
Intended Learning Outcomes
Subject options
Select to view your study options…
Bendigo, 2021, Semester 2, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorLydia Cui
Class requirements
Computer LaboratoryWeek: 32 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
LectureWeek: 30 - 42
One 2.00 hours lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment 1 - Data pre-processing and decision tree (1,200-words equivalent)Source code and a written report on data pre-processing and decision trees | Assignment | Individual | No | 20 | SILO2, SILO3, SILO5 |
Assignment 2 - Classification and Clustering (1,200-words equivalent) Source code and a written report on classification and clustering | Assignment | Individual | No | 20 | SILO2, SILO4, SILO5 |
One 3-hour examination (3,000-words equivalent)Hurdle requirement: To pass the subject, a pass in the examination is mandatory. | Central exam | Individual | Yes | 50 | SILO1, SILO2, SILO3, SILO4, SILO5 |
Completion of 9 laboratory class tasks (1,000-words total) | Other | Individual | No | 10 | SILO5 |
Melbourne (Bundoora), 2021, Semester 2, Blended
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorLydia Cui
Class requirements
Computer LaboratoryWeek: 32 - 43
One 2.00 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
LectureWeek: 30 - 42
One 2.00 hours lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
Assignment 1 - Data pre-processing and decision tree (1,200-words equivalent)Source code and a written report on data pre-processing and decision trees | Assignment | Individual | No | 20 | SILO2, SILO3, SILO5 |
Assignment 2 - Classification and Clustering (1,200-words equivalent) Source code and a written report on classification and clustering | Assignment | Individual | No | 20 | SILO2, SILO4, SILO5 |
One 3-hour examination (3,000-words equivalent)Hurdle requirement: To pass the subject, a pass in the examination is mandatory. | Central exam | Individual | Yes | 50 | SILO1, SILO2, SILO3, SILO4, SILO5 |
Completion of 9 laboratory class tasks (1,000-words total) | Other | Individual | No | 10 | SILO5 |