Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT
Duplock, Ray & Kelson, Neil A. (2010) Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT. In eResearch Australasia 2010 : 21st Century Research : Where Computing Meets Data, 8th-12th November 2010, RACV Royal Pines, Gold Coast, Queensland. (Unpublished)
In a seminal data mining article, Leo Breiman  argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions (, p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.
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|Item Type:||Conference Item (Poster)|
|Keywords:||data mining, statistical procedures, predictive modelling|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistics not elsewhere classified (010499)|
|Divisions:||Current > QUT Faculties and Divisions > Division of Technology, Information and Learning Support
Current > Research Centres > High Performance Computing and Research Support
|Deposited On:||07 Feb 2011 22:21|
|Last Modified:||09 Dec 2013 06:07|
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