DATA MINING

CSE5DMI

2020

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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Lydia Cui

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 5 - Masters

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: CSE1OOF OR CSE5CES OR CSE4OOF

Co-requisites: N/A

Incompatible subjects: CSE4DMI

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Introduction to Data Mining

Resource Type: Book

Resource Requirement: Recommended

Author: Tan, PN, Steinback, M & Kumar, V

Year: 2006

Edition/Volume: N/A

Publisher: MORGAN KAUFMANN

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Data Mining: Concepts and Techniques

Resource Type: Book

Resource Requirement: Recommended

Author: Jiawei Han, Micheline Kamber and Jian Pei

Year: 2011

Edition/Volume: N/A

Publisher: Morgan Kaufmann

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/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.

Bendigo, 2020, Semester 1, Blended

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Lydia Cui

Class requirements

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

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

Assessments

Assessment element	Comments	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	N/A	N/A	N/A	No	20	SILO2, SILO3, SILO5
Assignment 2 - Classification and Clustering (1,200-words equivalent)Source code and a written report on classification and clustering	N/A	N/A	N/A	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.	N/A	N/A	N/A	Yes	50	SILO1, SILO2, SILO3, SILO4, SILO5
Completion of 9 laboratory class tasks (1,000-words total)	N/A	N/A	N/A	No	10	SILO5

Melbourne (Bundoora), 2020, Semester 2, Blended