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Understanding Data Mining. Craig A. Stevens, PMP, CC craigastevens@westbrookstevens.com www.westbrookstevens.com. Examples of Classical Statistical Methods. Latitude 36.19N and Longitude -86.78W. Nashville, TN, USA. Y i = a + bx i + e. Multiple Regression.

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understanding data mining

Understanding Data Mining

Craig A. Stevens, PMP, CC

craigastevens@westbrookstevens.com

www.westbrookstevens.com

slide2

Examples of

Classical Statistical Methods

slide5

Multiple Regression

http://www.ats.ucla.edu/stat/sas/faq/spplot/reg_int_cont.htm

slide6

Multiple Regression

http://www.ats.ucla.edu/stat/sas/faq/spplot/reg_int_cont.htm

slide7

Multiple Regression

http://www.ats.ucla.edu/stat/sas/faq/spplot/reg_int_cont.htm

slide8

Multiple Regression

http://www.ats.ucla.edu/stat/sas/faq/spplot/reg_int_cont.htm

slide9

Multiple Regression

http://www.ats.ucla.edu/stat/sas/faq/spplot/reg_int_cont.htm

slide11

http://datamining.typepad.com/photos/uncategorized/livejournal.pnghttp://datamining.typepad.com/photos/uncategorized/livejournal.png

slide13

What is Data Mining?

  • The process of identifying hidden patterns, trends, and relationships in large quantities of data.

Why Do Data Mining?

  • To discover useful information for making decisions.
  • Too many variables for Classical Statistical methods to work.
    • Large Number of Records 108 - 1012
      • Gigabyte – Terabyte
    • High Dimensional Data
      • Lots of Variables (10 – 104 attributes)
slide24

Decision Trees for Predictive Modeling

Padraic G. Neville SAS Institute Inc. 4 August 1999

slide27

Data Mining Art found at http://datamining.typepad.com/data_mining/dataviz/page/2/

slide28

Data Mining Art found at http://datamining.typepad.com/data_mining/dataviz/page/2/

slide31

SurfStat

A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

Keith J. Worsley

slide33

Genealogical Tree

On You Tube

http://www.youtube.com/watch?v=CnniJR5Ah7g