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Data Mining with
The SAS System
Dr. John Brocklebank, SAS Institute Inc
Gerhard Held, SAS Institute Europe
Outline
1. Data Mining - Needs and Requirements
2. The SAS Data Mining Solution
3. Conclusion
Data Mining?
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Data Mining
• Data Mining is the Process of
selecting, exploring and modelling
• large Amounts of Data
• to uncover previously unknown
Patterns for Business Advantage
Data Mining Applications
Banking
Credit Authorization
Credit Card Fraud Detection
Portfolio Analysis
Customer Segmentation
Insurance & Health Care
Claim Analysis
Fraudulent Behavior
Telecommunications
Churn Management
Call Behaviour Analysis
Retail/Marketing
Market Basket Analysis
Database Marketing
Category Management
Targeted/cross marketing
Transportation, Networks,
Utilities
Loading Patterns
General
Pricing Analysis
Associations &
Demography
Data Mining - Needs and
Requirements
• Data Mining is a Process
• Data Mining involves close Co-operation of
IT, Business, and Data Miners
The SAS Data Mining
Solution
Business Problem
The SAS Data Mining
Solution
Business Problem
The SAS Data Mining Solution
- Currently • NNA - Initial Prod. on Win, OS/2, HP-UX,
AIX, SUN, Digital UNIX, ORLANDO I and II
• Tree Menue System (CHAID, later CART)
• Everything else Production Software:
– Exploration: INSIGHT, SPECTRAVIEW,
GIS
– Statistics
– Time Series Forecasting
– Market Research Methods
The SAS Data Mining Solution
New: SAS Enterprise Miner(TM)
A unique and full-scale Business Solution:
• IT: DW Access, Scalability
• Business Users:
Intuitive Interface and
Business Orientation
• Data Miners: Analytical Depth
and Flexibility
The SAS Enterprise Miner(TM)
Environment
• Graphical User Interface: Analytical solutions
based on SEMMA process using process flow
diagrams
• Existing SAS programs and applications can
be easily incorporated
• All ingredients of SAS Enterprise Miner in
particular the DMDB and all analytical
engines are exclusively available through this
Data Mining Solution.
Sample
• The Sampling Tool allows users to extract a
sample of their data using:
– simple random
- stratified
– Nth observation
- first N observations
– cluster
• The sampling tool also facilitates construction
of training DMDB’s, validation, and test data
sets
Data Mining Database
(DMDB)
• PROC DMDB is a procedure that builds a
data mining database (DMDB). The DMDB
consists of two parts:
– an efficient SAS data set (character variables
convered to integers)
– a catalog of meta data information that
characterizes target and input variables
• The DMDB is required before the data mining
analytical modules are run.
Data Mining Database
(DMDB)
• Numeric Variables: Summary statistics are
calculated and stored.
• Character Variables: Values are converted
to integer form and linked to the meta data
layer.
• Classification Variables: Levels and
frequencies for each variable are stored in
the meta data (mapping recipe).
Explore / Modify
• Advanced Visualization tools enable users
to explore their data graphically.
• The Outlier Filter tool allows users to
quickly identify and remove outliers from
their data set.
• The Transformation Tool facilitates the
creation of transformed variables to be
used in the construction of the DMDB and
the modeling process.
Model
• The SAS Data Mining Solution provides a
full range of modeling and evaluation
techniques including:
– Data Mining Regression
– Decision Trees
– Neural Networks
– Associations
• All modelling techniques are available as:
– Procedures
– Icons in process flow diagrams
The DMINE Procedure
• Developed by Dr. Jim Goodnight to perform
“true data mining” by:
– providing a fast preliminary variable
assessment
– facilitating quick development of predictive
models with large volumes of data
The DMINE Procedure
• PROC DMINE quickly identifies input
variables useful for predicting target variables
(“model screening”)
• Describes how they fit into a linear models
(regression/ANOVA) framework.
• Results from this procedure can be passed to
the Neural Network and Data Splits tools or to
any other procedure in the SAS System.
The DMINE Procedure
• Supports multiple target variables
• Constructs and evaluates up to two-factor
interactions
• Collapses levels of class variables using a
criterion based on the R-square value
The DMINE Procedure
• First step: simple linear regression model is
fit for each input variable.
• The input variables sorted in descending
order by the R-square values.
• Second step: forward selection regression
for all inputs including class, continuous
variables, grouped classes, and ANOVA16
variables
• Variables and factors used and not used in
final model are displayed
The DMINE Procedure
• Binary targets: logistic regression model is
fit to the data using the predictions from the
stepwise OLS run as a covariate.
• No logistic regression model is run for
interval target variables.
Data Mining Regression
• DMREG is a new procedure that allows the
user to access all of the functionality of REG
and LOGISTIC while including additional
functionality for data mining.
Data Mining Regression
• New features include:
– Uses DMDB as an input data source
– Handles training, validation, test and score data
sets
– Accepts both continuous and discrete variables
as inputs
– Accepts binary, continuous, or ordinal variables
as targets
Data Mining Regression
• Statistical methodologies and algorithms
supported include:
– Multiple linear regression
– Logistic regression
– Variable selection methods
– Multiple optimization methods
– Event and Event/Trial coding for classification
target variables
Data Mining Regression
• Model results and assessments are provided
in the form of:
– parameter estimates and related statistics
– goodness of fit statistics
Decision Trees
• DATA SPLITS is a new procedure that allows
users to construct classification and decision
trees.
• This procedure replaces the TREEDISC
macro and the SAS Tree Application.
Decision Trees
• Features include:
– Uses DMDB as an input data source
– Accepts both continuous and discrete input
and target variables
– Incorporates missing values for the inputs
into the modeling process
Decision Trees
• Statistical methodologies and algorithms
supported include:
– Utility functions defined for each alternative
decision
– Different fitting criteria for continuous and
discrete targets, CART, and CHAID
– Manual pruning of the tree in a graphical
environment
Decision Trees
• Model results and assessments are provided
in the form of:
– Utilities for rule assessment on both training
and validation data sets
– Goodness of fit statistics
– Interactive classification tree
– Interactive 3-D tree-ring graph
– Decision rules
Neural Networks
• NEURAL is a new procedure that allows
users to construct and train neural networks.
• This procedure replaces the TNN macros and
the SAS Neural Network Application.
Neural Networks
• Features include:
– Uses DMDB as an input data source
– Accepts both continuous and discrete input
and target variables
– Provides interactive network diagram for
construction of neural networks
Neural Network
• Statistical methodologies and algorithms
supported include:
– Construction of multi-layer feedforward
networks and radial basis functions
– Multiple training techniques including nonlinear optimization methods and
backpropagation
– User control over selection of activation and
objective functions
Neural Network
• Model results and assessments are provided
in the form of:
– Goodness of fit statistics including RMSE,
SBC, and AIC
– Misclassification tables for nominal outputs
Associations
• ASSOC and RULEGEN are new procedures
that allow users to discover associations
among items in a data base.
• Possible Applications:
– Market basket analysis
– Analysis of Web usage
– Bank transactions
Associations
• Features include:
– Uses DMDB as an input data source
– Discovers rules of the form:
· if item A is part of an event, then x% of the time,
item B is also part of the event
Associations
• Statistical methodologies and algorithms
supported include:
– Constructs rules containing a left-hand-side
(LHS) and a right-hand-side (RHS) based on
frequency counts for various combinations of
items
Associations
• Model results and assessments are provided
in the form of:
– Association rules
– Information statistics such as confidence,
support, and lift
– A user interface which allows users to sort the
rules by information statistics and to select both
the LHS and RHS rules
Model Management
and Assessment
• Users can assess results of modeling through
interactive assessment graphs, gains charts,
and profit and ROI graphs.
• A common interface for each modeling tool
allows the user to document and manage the
model development process.
The SAS Enterprise Miner(TM)
Architecture
Client-server Approach:
• Clients: Win 95, Win NT
• Servers Win NT, all major UNIX
• Mainframe as Data Server, later also
Compute Server
SAS System and Data Mining
Approx. Timeline
The SAS System for Data Mining
SAS Enterprise Miner
Restr.
alpha
Apr
SEUGI
SUGI/CEBIT
Feb
Jun
beta
Aug
prod
Oct
Dec
1997
Summary
The SAS Data Mining Solution is unique:
• IT: DW Access, Scalability
• Business Users:
Intuitive Interface and
Business Orientation
• Data Miners: Analytical Depth
and Flexibility
Data Mining with
The SAS System
Thank you for your Attention!
Questions?