1. Data Mining vs. Statistics
Pavel Brusilovsky

2. Objectives Intro to Data Mining Data Mining vs. Statistics
Data Mining vs. Text Mining
Applications of Data Mining 2

3. Data Mining Data Mining
is a cutting edge technology to analyze diverse, multidisciplinary and multidimensional complex data
Data mining could identify relationships in your multidimensional and heterogeneous data that cannot be identified in any other way
Successful application of state-of-the-art data mining technology to marketing and sales is indicative of analytic maturity and the success of a company
Working definition of Data Mining:
Data Mining is a process of discovering previously unknown and potentially useful hidden pattern in your data 3

4. What is the Taxonomy of Data Mining?
Data mining taxonomy, based on application
Data Mining
Text Mining
Web Mining
Image Mining…
Data mining taxonomy, based on the usage of domain knowledge:
Verification-driven data mining
Is associated with traditional quantitative approaches that permit a decision maker to express and verify organizational and personal domain knowledge
Discovery driven data Mining
It tied with knowledge discovery technology capable of automatically discovering previously unknown patterns hidden in the data
Combination of both classes leads to synergy that can produce meaningful and reliable results that may not be obtained within the framework of each class of data mining independently
Data mining taxonomy, based on estimation paradigm:
supervised learning
unsupervised learning 4

5. What is the deference between “Search” and “Discover”
Source: 5

6. Example: Amazon.com purchase suggestion
Amazon.com increased sales by 15%, using data/text mining generated purchase suggestions 6

7. Data Mining and Related Fields
Statistics: “The model is king” (Hand)
Data Mining: “The data is king” 7

8. Is Data Mining extension of Statistics?
Data Mining and Statistics: mutual fertilization with convergence
Statistical Data Mining (Graduate course, George Mason University)
Statistical Data Mining and Knowledge Discovery (Hardcover) by Hamparsum Bozdogan (Editor)
An overview of Bayesian and frequentist issues that arise in multivariate statistical modeling involving data mining 
Data Mining with Stepwise Regression (Dean Foster, Wharton School)
use interactions to capture non-linearities
use Bonferroni adjustment to pick variables to include
use the sandwich estimator to get robust standard errors 8

9. What are Data Mining Myths?
Myth 1: Data mining automatically discovers hidden pattern in your data
Myth 2: Data mining is design for business analysts who are not professional in quantitative fields
Myth 3: Data mining findings can be easily translated into decision-maker actions
Myth 4: Data mining encompasses decision analysis/decision support technology 9

10. What are logical steps of Data Mining
What are logical steps of Data Mining? SEMMA methodology (SAS Enterprise Miner)
The core process of conducting data mining study includes the following steps (SEMMA):
Sample
Explore
Modify
Model
Assess
SEMMA is a logical organization of the functional tool set of SAS Enterprise Miner for carrying out the core tasks of data mining
SEMMA is focused on the model development aspects of data mining 10

11. CRoss-Industry Standard Process for Data Mining (CRISP-DM)
SPSS Clementine
Six phases of CRISP-DM:
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Model deployment 11

12. Statistics vs. Data Mining: Concepts
Feature
Statistics
Data Mining
Type of Problem
Well structured
Unstructured / Semi-structured
Inference Role
Explicit inference plays great role in any analysis
No explicit inference
Objective of the Analysis and Data Collection
First – objective formulation, and then - data collection
Data rarely collected for objective of the analysis/modeling
Size of data set
Data set is small and hopefully homogeneous
Data set is large and data set is heterogeneous
Paradigm/Approach
Theory-based (deductive)
Synergy of theory-based and heuristic-based approaches (inductive)
Signal-to-Noise Ratio
STNR > 3
0 < STNR <= 3
Type of Analysis
Confirmative
Explorative
Number of variables
Small
Large 12

13. Statistics vs. Data Mining: Regression Modeling
Feature
Statistics
Data Mining
Number of inputs
Small
Large
Type of inputs
Interval scaled and categorical with small number of categories (percentage of categorical variables is small)
Any mixture of interval scaled, categorical, and text variables
Multicollinearity
Wide range of degree of multicollinearity with intolerance to multicollinearity
Severe multicollinearity is always there, tolerance to multicollinearity
Distributional assumptions, homoscedasticity,
outliers, missing values
Intolerance to distrubitional assumption violation, homoscedasticity,
Outliers/leverage points, missing values
Tolerance to distributional assumption violation, outliers/leverage points, and missing values
Type of model
Linear / Non-linear / Parametric / Non-Parametric in low dimensional X-space (intolerance to uncharacterizable non-linearities)
Non-linear and non-parametric in high dimensional X-space with tolerance to uncharacterizable non-linearities 13

14. Well-structured Business Problem Unstructured Business Problem
What is an unstructured problem?
Well-structured Business Problem
Unstructured Business Problem
Definition
Can be described with a high degree of completeness
Cannot be described with a high degree of completeness
Can be solved with a high degree of certainty
Cannot be resolved with a high degree of certainty
Experts usually agree on the best method and best solution
Experts often disagree about the best method and best solution
Can be easily and uniquely translated into quantitative counterpart
Cannot be easily and uniquely translated into quantitative counterpart
Goal
Find the best solution
Find reasonable solution
Complexity
Ranges from very simple to complex
Ranges from complex to very complex 14

15. What are differences between Data/Text Mining and Statistics?
Statistical analysis is designed to deal with structured data in order to solve structured problem:
Results are software and researcher independent
Inference reflects statistical hypothesis testing
Data mining is designed to deal with structured data in order to solve unstructured business problems
Results are software and researcher dependent (absence of implementation standards)
Inference reflects computational properties of data mining algorithm at hand
Text mining is designed to deal with unstructured data in order to solve unstructured problems
Results are software and researcher dependent
Inference reflects computational properties and visualization capability of text mining algorithm at hand 15

16. When data mining technology is appropriate?
Data mining technology is appropriate if:
The business problem is unstructured
Accurate prediction is more important than the explanation
The data include the mixture of interval, nominal, ordinal, count, and text variables, and the role and the number of non-numeric variables are essential
Among those variables there are a lot of irrelevant and redundant attributes
The relationship among variables could be non-linear with uncharacterizable nonlinearities
The data are highly heterogeneous with a large percentage of outliers, leverage points, and missing values
The sample size is relatively large
Important marketing and sales studies/projects have the majority of these features 16

17. Accurate prediction is more important than the explanation17

18. What is Breiman Uncertainty Principle?
Accuracy * Interpretability = Breiman’s constant
Breiman uncertainty principle means that
The higher method’s accuracy, the lower its interpretability, and vice versa 18

19. What are great Data Mining Ideas?
Injecting randomness into function estimation procedure
Bagging (Breiman, 1996):
Apply the same unstable algorithm to different samples (with replacement) of the original data
Different samples yield different models
The average of the predictions of these models might be better than the predictions from any single model
Boosting (Friedman, Hastie, and Tibshirani (1999):
Each model is based on the same original data
The first individual model is fit to the original data
For the second model, subtract the predicted value from the original target value, and use the difference as the target value to train the second model
For the third model, subtract weighted average of the predictions from the original target value, and use the difference as the target value to train the third model, and so on. 19

20. What are the best Data Mining Conferences?
Annual SAS Data Mining Technology Conference
The world’s largest data mining conference that balancing theory and practice
Annual International Conference on Knowledge Discovery and Data Mining (KDD)
Sponsored by the American Association for Artificial Intelligence (AAII)
Annual International Salford Systems Data Mining Conference
Focusing on solving real world challenges
Business Applications of CART, MARS, TreeNet, and Random Forrest
Keynote speakers: Jerome Friedman (Stanford University) and Leo Breiman (University of California, Berkeley) 20

21. What are the best data mining tools?
Salford Systems’ Tools (CART, Random Forest, MARS, TreeNet)
SAS Enterprise Miner/Text Miner
SPSS Clementine
Megaputer Intelligence PolyAnalyst 21

22. Reference (Data Mining)
Randall Matignon (2007), Neural Network Modeling Using SAS Enterprise Miner , SAS® Institute Inc.
David J. Hand, Data Mining: Statistics and More? The American Statistician, May 1998, Vol. 52 No. 2
Friedman, J.H Data Mining and Statistics. What’s connection? Proceedings of the 29th Symposium on the Interface: Computing Science and Statistics, May 1997, Houston, Texas
Doug Wielenga (2007), Identifying and Overcoming Common Data Mining Mistakes, SAS Global Forum Paper
Nathan Treloar (2002), Text Mining: Tools, Techniques, and Applications 22