Course Title: Data Mining

Part A: Course Overview

Course Title: Data Mining

Credit Points: 12

Course Coordinator: Dr. Jenny Zhang

Course Coordinator Phone: +61 3 9925 2774

Course Coordinator Email:xiuzhen.zhang@rmit.edu.au

Pre-requisite Courses and Assumed Knowledge and Capabilities

You may not enrol in this course unless it is explicitly listed in your enrolment program summary, and you have confirmed with your program coordinator that it is an appropriate choice for your study plan.

To successfully complete this course, you should have the ability to solve fundamental problems in computing including relational databases and programming. You should have completed one of the following courses (or provide evidence of equivalent capabilities): Java for Programmers or Programming 2 or Java for C Programmers.

Course Description

This course is about applying data analysis techniques to large data repositories. It aims to provide you with up-to-date conceptual and practical knowledge on recent developments in data mining. At the end of this course, students will understand concepts, principles and techniques of data mining. For practical work, you will be using a popular data mining package to analyse data of various formats, including supermarket transaction data, relational data and textual data.

Lecture topics include: Overview of knowledge discovery and data mining, data mining techniques and their evaluation -- including classification, Clustering and semi-supervised learning, Association analysis, and text data analysis.
This is an elective course for the Honours year in Computer Science and is part of the Advanced Database cluster for the coursework Masters degrees in the School of Computer Science and Information Technology. This course is also available to later year undergraduate students with an interest in advanced data analysis technologies.

Objectives/Learning Outcomes/Capability Development

Development of student graduate capabilities is an on-going process that takes place in all courses and over the period of the whole program. This course particularly addresses the following capabilities: enabling knowledge (machine learning and data mining ), critical analysis (in undertaking data mining exercises, and in setting up and managing data mining applications), and responsibility (appropriate use of stored data, especially business data ).

On completion of this course you should have gained a good understanding of basic concepts, principles and techniques of data mining. Specifically, you should be able to:

• define what knowledge discovery and data mining is
• recognise the key areas and issues in data mining
• develop an in-depth understanding of classification models
• develop an in-depth understanding of clustering models
• develop an in-depth understanding of association analysis
• apply various learning techniques to analyse real-word large data repositories
• apply evaluation metrics to select data mining techniques

Overview of Learning Activities

The learning activities included in this course are:

• key concepts will be explained in lectures, classes or online, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples;
• tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and other students, and give feedback on your progress and understanding;
• assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
• private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.

Overview of Learning Resources

You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through the Learning Hub (also known as online@RMIT) and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.

Use the RMIT Bookshop’s textbook list search page to find any recommended textbook(s).

Overview of Assessment

The assessment for this course comprises of assignments and a formal written examination.
 
For standard assessment details, including deadlines, weightings, and hurdle requirements relating to Computer Science and IT courses see: http://www.rmit.edu.au/compsci/cgi