DATA MINING

CSE4DMI

2015

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 multimedia, spatial and temporal data and biological data such as DNA sequences. Data Mining has evolved from several areas including: databases, artificial intelligence, algorithms, information retrieval and statistics. This unit is designed to provide graduate students with a solid understanding of data mining concepts and tools. The unit covers classification rule extraction, clustering algorithms and association rule mining techniques. Domain applications of data mining techniques will be addressed in this unit.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorPhoebe Chen

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Data MiningRecommendedTan, PN, Steinback, M & Kumar, V1ST EDN, MORGAN 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.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Ethical Awareness (Ethical Awareness)

02. Explain the major components and issues in data mining techniques, such as pattern discovery through classification systems, clustering algorithms, and association analysis.

Activities:
Lectures 2, 3, 4, 5, 6 are on data description, similarity metrics, dimensionality issue, classification techniques including decision tree, k-NN, neural networks approaches. Lectures 7 is on some basics of association analysis, including definitions of association rule mining, rule evaluation criteria (support and confidence), two step approach for generating rules, and Apriori algorithm. Lecture 8 and 9 are on clustering techniques for pattern extraction and knowledge discovery from unlabeled data.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Writing (Writing)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)
Inquiry/ Research (Inquiry/ Research)

03. Evaluate data mining algorithms, i.e. classification systems, important issues in clustering algorithms, and the links between data mining and knowledge engineering.

Activities:
Lectures 10 is a revision lecture where all relevant data mining algorithms will be evaluated for deeper understandings and applications. Also, students will find the links between data mining and knowledge engineering for their further studies.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Inquiry/ Research (Inquiry/ Research)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

04. Implement a data mining techniques for computational discovery of selected datasets.

Activities:
Lab 1 to Lab 3 are on basics of Matlab programming and feature extraction of DNA data, where students will learn how to use Matlab tools to implement data pre-processing for solving data mining problems. Lab 4 and Lab 5 are on practice of the well-known data mining tool-C4.5, which will be used for rule-based data classification. Lab 6 is on neural network based classification system design. Assignment consultations will be given in Lab 7 and Lab 8, where students will get help and technical support to overcome their difficulties encourted in doing their assessments.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)

Subject options

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Start date between: and    Key dates

Melbourne, 2015, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorPhoebe Chen

Class requirements

Computer Laboratory Week: 31 - 43
One 2.0 hours computer laboratory per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

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.

Assessments

Assessment elementComments% ILO*
Assignemnt 1 equivalent to 1,200 wordsHurdle requirement: In order to pass the unit, students must obtain an overall pass grade, pass the examination and pass the overall non-examination components.20 04
Assignemnt 2 equivalent to 1,200 words20
one 3-hour examination50 01, 02, 03
pratical/tutorial participation and contribution to tutorial taks10