Credit points: 15
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.
FacultyFaculty of Science, Tech & Engineering
Subject Co-ordinatorPhoebe Chen
Available to Study Abroad StudentsYes
Subject year levelYear Level 4 - UG/Hons/1st Yr PG
|Resource Type||Title||Resource Requirement||Author and Year||Publisher|
|Readings||Introduction to Data Mining||Recommended||Tan, PN, Steinback, M & Kumar, V||1ST EDN, MORGAN KAUFMANN|
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Melbourne, 2014, Semester 2, Day
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorPhoebe Chen
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
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.
|Assignemnt 1 equivalent to 1,200 words||Hurdle 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|
|Assignemnt 2 equivalent to 1,200 words||20|
|one 3-hour examination||50|
|pratical/tutorial participation and contribution to tutorial taks||10|