Math3346 -- Data Mining

Maths3346 -- Data Mining

In 2006 John Maindonald and Graham Williams shared responsibilty for this course.

Data mining gives a new twist on data deployment and analysis methodologies that have been developed over the past century or more. Recent developments have included:

Huge increases in computational power and in computer storage
A synergy between theoretical and algorithmic advances, advances in software and in computational power
Integration of what were formerly stand-alone abilities into single software systems with a single interface and command language. (The R system is a prime example; see below)
New types of data, and new opportunites for collecting data, arising from advances in instrumentation, from the internet and from widespread deployment of databases. Chapter 5 of Ayres (2007), entitled "Why now?", is a good summary of the impact of such advances.

Classification is a major pre-occupation of data mining, with a more limited focus on regression with a continuous outcome variable. The aim is typically prediction rather than the more challenging task of interpretation of model parameters.

Students intending to take this course will it useful to come with some initial familiarity with the open-source R system. The R system is available without charge for downloading from the internet. It is a marvelous example of what can be achieved when specialists co-operative internationally, using the internet for communication.

Reference

Ian Ayres 2007."Super Crunchers. Why Thinking-By-Numbers is the New Way to be Smart". Bantam

Maths3346 -- Data Mining

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