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Transcript
DATA MINING
Patrick J. Gallagher
March 21, 2006
What is Data Mining?
DEFININTION:
The automated extraction of hidden
predictive information from
(large) databases.
DATA MINING TECHNIQUES

CLASSICAL:
1. Statistics
2. Nearest Neighborhoods
3. Clustering

NEXT GENERATION:
1. Decision Trees
2. Neural Networks
3. Rule Induction
THE CLASSICS


Techniques discussed will be those that
have been used for decades
They have also used almost all of the time
on existing business problems
1. STATISTICS
WHAT IS IT
STATISTICS: branch of mathematics
concerning the collection and the
description of data



Born of real world problems from business,
biology and gambling
Knowing statistics helps the average
business person make better decisions by
allowing them to figure out risk and
uncertainty when all facts either aren’t
known or can be collected
Has been around for a long, long time
(easily a century)
HISTOGRAMS
STATISTICAL SUMMARIZATION
Database Information
ID
1
2
3
4
5
6
7
8
9
10
NAME
Prediction
Age
Balance
Income
Eyes
Gender
Amy
NO
62
$0
Medium
Brown
F
Al
NO
53
$1,800
Medium
Green
M
Betty
NO
47
$16,543 High
Brown
F
Bob
YES
32
$45
Medium
Green
M
Carla
YES
21
$2,300
High
Blue
F
Carl
NO
27
$5,400
High
Brown
M
Donna
YES
50
$165
Low
Blue
F
Don
YES
46
$0
High
Blue
M
Edna
YES
27
$500
Low
Blue
F
Ed
NO
68
$1,200
Low
Blue
M
This histogram shows the number of customers with
various eye colors. As you can see, the histogram can
show important information about the database.
WHAT QUESTIONS CAN
STATISTICS ANSWER?




What patterns are there in my database?
What is the chance that an event will
occur?
What patterns are significant?
What is a high level summary of the data
that gives me some idea of what is
contained in my database
NOT ALL HISTOGRAMS ARE THIS
SIMPLE
Complex histograms provide more
information (Predictors)
SUMMARY STATISTICS






Max - max value of predictor
Min - minimum value of predictor
Mean - average value of predictor
Median - value for a given predictor that
divides the database as nearly as possible
into two databases of equal number of
records.
Mode – common value for the predictor
Variance – measure of how spread out the
values are from the average value
STATISTICS FOR PREDICTION
Prediction = ?

A = Regression

B = Simulation

C = Decision
PREDICTION = REGRESION
“A”
LINEAR REGRESSION


One predictor and a prediction. The
relationship between the two can be
mapped on a two dimensional space and
the records plotted for the prediction
values along the Y axis and the predictor
values along the X axis
Seeks to build a predictive model that is a
line that maps between each predictor
value to a prediction value.
Sample Linear Regression
Predictive Model



The line will take a given
value for a predictor and
map it into a given value
for a prediction
Equation is:
Prediction=a+b*predictor
Trick with predictive
modeling is to find the
model that best
minimizes the error
2. NEAREST NEIGHBOR
What does it mean?
Nearest Neighbor
In order to predict what a
prediction value is in one record
look for records with similar
predictor values in the historical
database and use the prediction
value from the record that it
“nearest” to the unclassified
record
NEAREST NEIGHBOR
EXAMPLE ?
EXAMPLE
Determining that people in your
neighborhood have an income of over
$100,000 per year
NEAREST NEIGHBOR ASSUMES
Your income is also over $100,000

Prediction for a prediction value in one record is
determined by looking for similar predictor
values in the historical database and use the
prediction value from the record that is nearest
to the unclassified record



(ex: salaries of people in your neighborhood)
The techniques are among the easiest to use
and understand because the techniques work
similar to the ways a person thinks
Are among the oldest techniques used in data
mining.
NEAREST NEIGHBOR
PREDICTION TECHNIQUE
USES
Business
Stock Market Data
PREDICTION
IN NEAREST NEIGHBOR
MEANS:
Objects that are “NEAR” to each other will
have similar prediction values as well.
Thus if you know the prediction value of one
of the objects you can predict it for its
nearest neighbor.
BUSINESS
Text Retrieval :This particular technique is
used to find other documents that share
important characteristics with those
documents that have been marked as
interesting.
STOCK MARKET DATA
The input data is just a long series of stock prices
over time without any particular record that
could be considered to be an object.
Example:
Predictor Values
10: 12: 14: 15: 10: 13: 11: 14: 15:
Prediction Value
11 (10th number)
3. CLUSTERING
What does it mean?
CLUSTERING
Clustering is a method which like records
are grouped together in order to give the
end user a high level view of what is going
on in the data base and business.
CLUSTERING
In the real world
Two clustering systems are the PRIZM™ system
from Claritias Corporation and MicroVision™
from Equifax Corporation. These companies
have grouped the population by demographic
information into segments that they believe are
useful direct marketing and sales.
NAME
INCOME AGE EDUCATION VENDOR
Wealthy
35-54
College
Claritas
Prism™
Shot Gun and
Pickups
Middle
35-64
High School
Claritas
Prism™
Southside City
Poor
Mix
Grade School
Claritas
Prism™
Living Off the
Land
Middle – Poor
School
Age
Families
Low
Equifax
MicroVision™
University USA
Very Low
YoungMix
Medium - High
Equifax
MicroVision™
Sunset Years
Medium
Seniors
Medium
Equifax
MicroVision™
Blue Blood
Estates
CLUSTERING VS NEAREST
NEIGHBOR
Nearest Neighbor



Used for prediction as well as
consolidation
Space is defined by the problem to be
solved. (Supervised learning
technique)
Generally only uses distance metrics to
determine nearness.
Clustering



Used mostly for consolidating data into
a high-level view and general grouping
of records into like behaviors.
Space is defined as default ndimensional space, or is defined by the
user or is a predefined space driven by
past experience. (Unsupervised
learning technique)
Can use other metrics besides distance
to determine nearest of two records –
for example linking two points
together.
What are the two main types of
Clustering techniques?
HIERARCHICAL
NON-HIERARCHICAL
CLUSTERING
HIERARCHY
of
CLUSTERS
NON- HIERARCHIAL
CLUSTERING
1. Single Pass Methods
2. Reallocation Methods
Hierarchical Clustering


It is created by starting either at the top and
subdividing (dividing clustering) or starting at
the bottom with as many clusters as there are
records and merging (agglomerative clustering).
Has advantage over non-hierarchical in that the
clusters are solely by the data and that the
number of clusters can be increased or
decreased by simply moving up and down the
hierarchy.
NEXT GENERATION


Represent the most often used techniques
that have been developed over the past
two decades of research.
It can be used for either discovering new
information within large databases or for
building predictive models.
NEXT GENERATION
1. DECISION TREES
2. NEURAL NETWORK
3. RULE INDUCTION
1. DECISION TREES
What are they ?
DECISION TREES
A predictive model that, as its name implies,
can be viewed as a tree. Specifically, each
branch of the tree is a classification
question and the leaves of the tree are
partitions of the dataset with their
classification
DECISION TREE
EXAMPLE
DECISION TREE
HISITORY


Similar technologies have been around for
almost 20 years and early versions of the
algorithms date back in the 1960s
Originally, these techniques were
developed for statisticians to automate the
process of determining which fields in
their database were actually useful or
correlated with the particular problem that
they were trying to understand.
DECISION TREE
USES



EXPLORATION – looks at predictors and values
that are chosen for each split of the tree. Often
times, these predictors provide usable insights
or propose questions that need to be answered.
DATA PREPROCESSING – can be used on the
first pass of data mining to create a subset of
useful predictors that can be used in neural
networks, nearest neighbor and normal
statistical routines.
PREDICTION – used as a by product by
statisticians because decision trees are used for
exploratory analysis.
DECISION TREE
ALGORITHMS
ID3
CART
CHAID
ID3




Developed in late 1970’s by J. Ross Quinlan
First Decision Tree algorithm
 Based on previous inference systems and
concept learning systems from decades
preceding.
Initial used for game playing strategies for chess
games.
Picks predictors and splitting values based on
“gain” and information that the split/s provide.

The difference between the entropy of the original
segment and the accumulated entropies of the
resulting split segments.
ID3 to C4.5
ENHANCEMENTS




Predictors with missing values can still be
used.
Predictors with continuous values can be
used.
Pruning is introduced
Rule derivation
CART



Stands for Classification and Regression Trees
Data exploration and prediction algorithm
developed by Leo Breiman, Jerome Friedman,
Richard Olshen and Charles Stone.
Each predictor is picked on how well it teases
apart the records with different predictions.
CHAID


Stands for Chi Square Automatic
Interaction Detector
Similar to CART


It builds a decision tree
Different from CART

In the way it chooses its splits.
2. NEURAL NETWORKS
What is it?
NEURAL NETWORK
Computer programs implementing
sophisticated pattern detection and
machine learning algorithms on a
computer to build predictive models
from large historical databases.



Artificial neural networks derive their name from their
historical development which started off with the
premise that machines could be made to “think” if
scientists found ways to mimic the structure and
functioning of the human brain on the computer.
Greatest breakthroughs in neural networks in recent
years have been in there application to more mundane
real world problems like customer response prediction or
fraud detection.
They technically are considered to “learn” and make
better predictions by detecting patterns using analogies
in similar ways that humans do.
NEURAL NETWORK
USES
Clustering
Outlier Analysis
Example: Wine Distributor
Wine distributor store stands out as making significantly lower
profit. Upon further examination the distributor was delivering
product but not collecting payment.
Feature Extraction
Neural Networks
(Components)
Node- corresponds to the neuron in the
human brain.
Link- it corresponds to the connections
between neurons.
NEURAL NETWORK
Sample
NEURAL NETWORK
TYPES



Back Propagation- refers to the propagation of
the error backwards from the output nodes
through the hidden layers and to the input layer.
Kohonen Feature Maps- developed in the 1970’s
and are feed forward Neural Network generally
with no hidden layer.
- Used for unsupervised learning and clustering.
Radial Basic Function – represent a hybrid
between nearest neighbor and neural network
classification.
- Used for supervised and learning
3. RULE INDUCTION
What is it?
RULE INDUCTION
Is one of the major forms of data mining
and the most common form of knowledge
discovery in unsupervised learning systems.
It mines for a rule that is “interesting”.


It is a massive undertaking were all
possible patterns are systematically pulled
out of the data and then an accuracy and
significance are added to them that tell
the user how strong the pattern is and
how likely it is to occur again.
Rule induction systems are highly
automated and are probably the best of
data mining techniques for exposing all
possible predictive patterns in a database
Neural Network
VS
Rule Induction
NEURAL NETWORKS

Extremely proficient and
saying exactly what must
be done in a predictive
task with little
explanation.
Example- Who do I give
credit to and who do I
deny credit to.
RULE INDUCTION

When used for prediction,
they are like having a
committee of trusted
advisors each with a
slightly different opinion
as to what to do but
relatively well grounded
reasoning and a good
explanation for why it
should be done.
What is a RULE?
“if this and this and this then this.”
EXAMPLES
- If paper plates then plastic forks.
- If dip then potato chips.
Presenter
Dr. Balaji Padmanabhan
Assistant Professor of Operations and Information
Management
The Wharton School, University of Pennsylvania
Teaches:
 Enabling (Information) Technologies
 Data Mining / Decision Support Systems
 Introduction to the Computer as an Analysis Tool