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 ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesCSE1OOF OR CSE4OOF OR CSE5CES
CSE1OOF or CSE4OOF or CSE5CES 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

Readings

Introduction to Data Mining

Resource TypeRecommended

Resource RequirementN/A

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 TypeRecommended

Resource RequirementN/A

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

01. Perform critical and effective data- pre-processing tasks.
02. Evaluate major data mining classification methodologies.
03. Critique association rules mining approaches.
04. Evaluate Data Mining Algorithms based on data clustering techniques.
05. Apply advanced data mining techniques for pattern discovery from selected datasets.

Subject options

Select to view your study options…

Start date between: and    Key dates

Bendigo, 2021, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLydia Cui

Class requirements

Computer Laboratory Week: 10 - 22
One 2.00 h computer laboratory per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
One 2.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% 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

N/AN/AN/ANo20 SILO2, SILO3, SILO5

Assignment 2 - Classification and Clustering (1,200-words equivalent)Source code and a written report on classification and clustering

N/AN/AN/ANo20 SILO2, SILO4, SILO5

One 3-hour examination (3,000-words equivalent)Hurdle requirement: To pass the subject, a pass in the examination is mandatory.

N/AN/AN/AYes50 SILO1, SILO2, SILO3, SILO4, SILO5

Completion of 9 laboratory class tasks (1,000-words total)

N/AN/AN/ANo10 SILO5

Melbourne (Bundoora), 2021, Semester 2, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorLydia Cui

Class requirements

Computer Laboratory Week: 32 - 43
One 2.00 h computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

Lecture Week: 30 - 42
One 2.00 h lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% 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

N/AN/AN/ANo20 SILO2, SILO3, SILO5

Assignment 2 - Classification and Clustering (1,200-words equivalent)Source code and a written report on classification and clustering

N/AN/AN/ANo20 SILO2, SILO4, SILO5

One 3-hour examination (3,000-words equivalent)Hurdle requirement: To pass the subject, a pass in the examination is mandatory.

N/AN/AN/AYes50 SILO1, SILO2, SILO3, SILO4, SILO5

Completion of 9 laboratory class tasks (1,000-words total)

N/AN/AN/ANo10 SILO5