Data Mining vs. Statistics

Pavel Brusilovsky


* Intro to Data Mining
* Data Mining vs. Statistics
* Data Mining vs. Text Mining
* Applications of Data Mining

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

What is the difference between "Search" and "Discover"



Example: purchase suggestion

 Example: purchase suggestion JPG

Data Mining and Related Fields


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

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

What are the 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

CRoss-Industry Standard Process for Data Mining (CRISP-DM)

SPSS Clementine

Six phases of CRISP-DM:

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Model deployment

Statistics vs. Data Mining: Concepts


Statistics vs. Data Mining: Regression Modeling


What is an unstructured problem?


What are the differences between Data/Text Mining and Statistics?

  • Statistical analysis is designed to deal with structured data in order to solve structured problems:
  • 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

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

Accurate prediction is more important than the explanation


What is Breiman Uncertainty Principle?

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

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.

What are the best Data Mining Conferences?

Annual SAS Data Mining Technology Conference
  • The world's largest data mining conference that balances 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)

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

References (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. 1997. Data Mining and Statistics. What's the 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 073-2007
Nathan Treloar (2002), Text Mining: Tools, Techniques, and Applications