DATA MINING

CSE5DMI

2018

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 graduate students with a solid understanding of data mining concepts and tools. The subject covers data preprocessing, data classification, association rule mining, and clustering algorithms and techniques. Domain applications of data mining techniques will be addressed in this subject.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorPhoebe Chen

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites CSE1OOF or CSE4OOF or CSE5CES or equivalent (discuss with subject coordinator)

Co-requisitesN/A

Incompatible subjects CSE4DMI

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Data MiningRecommendedTan, PN, Steinback, M & Kumar, V; 2006MORGAN KAUFMANN
ReadingsData Mining: Concepts and TechniquesRecommendedJiawei Han, Micheline Kamber and Jian Pei; 2011Morgan Kaufmann

Graduate capabilities & intended learning outcomes

01. Explain the technologies and applications of data mining techniques (DM).

Activities:
Lecture 1 is on the introduction of data mining techniques and its applications.

02. Identify various data types and perform critical and effective data-preprocessing tasks

Activities:
Students will learn different types of data and their related issues such as sampling, similarity metrics, feature selection, dimensionality issue. They also learn and practise effective data-preprocessing techniques in Lecture 2, Tutorial 3, and assignment.

03. Explain and evaluate major classification methods

Activities:
Lectures 3, 4, 5, and 6 provide details for a wide range of classification approaches such as decision tree, rule-based classification, nearest neighbour classification, Bayes classification, artificial neural network (ANN), and support vector machine (SVM). Related issues covering underfitting and overfitting will also be discussed. Students will also apply various classification approaches to different datasets in Tutorials 4, 5, 6, and assignment.

04. Explain and evaluate association rules mining approaches

Activities:
Lectures 7 and 8 introduce association analysis for transaction data, including frequent itemsets, association rule mining, rule generation and evaluation, and Apriori algorithm. Students will practise association rules mining in Tutorials 7, 8, and assignment.

05. Explain and evaluate data clustering techniques

Activities:
Lectures 9, 10, and 11 introduce major data clustering techniques, such as K-means clustering, hierarchical clustering, and DBSCAN, for pattern extraction and knowledge discovery from unlabeled data. Students will learn how to applies these approaches to real datasets in Tutorials 9, 10, and assignment.

06. Discuss advanced issues of data mining algorithms, e.g. classification systems, important issues in clustering algorithms, and the links between data mining and knowledge engineering.

Activities:
Lectures 12 is a revision lecture where all relevant data mining algorithms will be evaluated for deeper understandings and applications. Also, students will analyse advanced issues such as classifications systems, and the links between data mining and knowledge engineering, for their further studies.

07. Implement advanced data mining techniques for pattern discovery from selected datasets.

Activities:
Students will learn the basics of MATLAB programming and WEKA in Tutorials including data pre-processing, decision tree, ANN, SVM,and association rules mining. Students will apply k-means and hierarchical approach for MATLAB data clustering. Assignment consultations will be given in certain tutorials, where students will get help and technical support to overcome their difficulties encountered in doing their assessments.

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 2, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorPhoebe Chen

Class requirements

Lecture Week: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Computer Laboratory Week: 32 - 43
One 2.0 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
Assignment 1 equivalent to 1,200 words20 02, 03, 07
Assignment 2 equivalent to 1,200 words20 02, 04, 05, 07
One 3-hour examinationHurdle requirement: To pass the subject, a pass in the examination is mandatory.50 01, 02, 03, 04, 05, 06
Tutorial participation and contribution to tutorial tasks equivalent to 100 words per tutorial10 07