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
Links
-
Detailed syllabus and course description
-
Course materials for Math3346 in 2007
-
Suggestions for getting started on R
-
Data mining -- John Maindonald's talks and papers