Business Intelligence and Analytics Systems for Decision .docx

Business Intelligence and Analytics:
Systems for Decision Support
(10th Edition)
Chapter 5:
Data Mining
Business Intelligence and Analytics:
Systems for Decision Support
(10th Edition)
Copyright © 2014 Pearson Education, Inc. 5-2
Learning Objectives
for business intelligence
business analytics and data mining
nge of applications of data
mining

-DM
Learning Objectives
preprocessing for data mining
mining
software tools
mining
Opening Vignette…
Cabela’s Reels in More Customers with
Advanced Analytics and Data Mining

Questions for the
Opening Vignette
1. Why should retailers, especially omni-channel
retailers, pay extra attention to advanced
analytics and data mining?
2. What are the top challenges for multi-channel
retailers? Can you think of other industry
segments that face similar problems/challenges?
3. What are the sources of data that retailers such
as Cabela’s use for their data mining projects?
4. What does it mean to have a “single view of the
customer”? How can it be accomplished?

Data Mining Concepts/Definitions
Why Data Mining?
ty of quality data on customers, vendors,
transactions, Web, etc.
into data warehouses.
storage capabilities; and decrease in cost.
toward conversion of information
resources into nonphysical form.
Definition of Data Mining
novel, potentially useful, and ultimately
understandable patterns in data stored in
structured databases. - Fayyad et al., (1996)
valid, novel, potentially useful, understandable.

ction, pattern
analysis, knowledge discovery, information
harvesting, pattern searching, data dredging,…
S
ta
tis
tic
s
Management Science &
Information Systems
A
rtificia
l In
te
llig
e
n
ce

Databases
Pattern
Recognition
Machine
Learning
Mathematical
Modeling
DATA
MINING
Data Mining is at the Intersection
of Many Disciplines
ed
data warehouse (not always!).
-server or a
Web-based information systems architecture.
may include soft/unstructured data.

are essential (Web, Parallel processing, etc.)
Data Mining
Characteristics/Objectives
Application Case 5.1
Smarter Insurance: Infinity P&C Improves
Customer Service and Combats Fraud with
Predictive Analytics
Questions For Discussion
1. How did Infinity P&C improve customer service
with data mining?
2. What were the challenges, the proposed solution,
and the obtained results?
3. What was their implementation strategy? Why is it
important to produce results as early as possible
in data mining studies?
Data in Data Mining

ta: a collection of facts usually obtained as the result of
experiences, observations, or experiments.
and knowledge are derived).
Data
Categorical Numerical
Nominal Ordinal Interval Ratio
Structured
Unstructured or
Semi-Structured
MultimediaTextual HTML/XML
or
symbolic) relationship among data items

What Does DM Do?
How Does it Work?
Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis
Police Department Pinpoint Crime and
Focus Police Resources
1. How did the Memphis Police Department
used data mining to better combat crime?
2. What were the challenges, the proposed
solution, and the obtained results?
A Taxonomy for
Data Mining Tasks
Data Mining
Prediction

Classification
Regression
Clustering
Association
Link analysis
Sequence analysis
Learning Method Popular Algorithms
Supervised
Supervised
Supervised
Unsupervised
Unsupervised
Unsupervised
Unsupervised
Decision trees, ANN/MLP, SVM, Rough
sets, Genetic Algorithms
Linear/Nonlinear Regression, Regression
trees, ANN/MLP, SVM

Expectation Maximization, Apriory
Algorithm, Graph-based Matching
Apriory Algorithm, FP-Growth technique
K-means, ANN/SOM
Outlier analysis Unsupervised K-means, Expectation
Maximization (EM)
Apriory, OneR, ZeroR, Eclat
Classification and Regression Trees,
ANN, SVM, Genetic Algorithms
Data Mining Tasks (cont.)
-series forecasting
ualization
-driven data mining

-driven data mining
Data Mining Applications
ze return on marketing campaigns
-, up-selling)
etecting fraudulent transactions
-, up-selling)

Data Mining Applications (cont.)
ptimize inventory levels at different locations
chinery failures
the use manufacturing capacity

Increasingly more
popular application areas
for data mining
A Mine on Terrorist Funding
1. How can data mining be used to fight
terrorism? Comment on what else can be
done beyond what is covered in this short
application case.
2. Do you think data mining, while essential
for fighting terrorist cells, also jeopardizes
individuals’ rights of privacy?

Data Mining Process
-DM (Cross-Industry Standard Process
for Data Mining)
Assess)
Data Mining Process
Source: KDNuggets.com
Data Mining Process: CRISP-DM
Data Sources
Business

Understanding
Data
Preparation
Model
Building
Testing and
Evaluation
Deployment
Data
Understanding
6
1 2
3
5
4
Data Mining Process: CRISP-DM

Step 1: Business Understanding
Step 2: Data Understanding
Step 3: Data Preparation (!)
Step 4: Model Building
Step 5: Testing and Evaluation
Step 6: Deployment
experimental (DM: art versus science?)
Accounts for
~85% of total
project time
Data Preparation – A Critical DM
Task
Data Consolidation
Data Cleaning
Data Transformation
Data Reduction

Well-formed
Data
Real-world
Data
· Collect data
· Select data
· Integrate data
· Impute missing values
· Reduce noise in data
· Eliminate inconsistencies
· Normalize data
· Discretize/aggregate data
· Construct new attributes
· Reduce number of variables
· Reduce number of cases
· Balance skewed data

Data Mining Process: SEMMA
Sample
(Generate a representative
sample of the data)
Modify
(Select variables, transform
variable representations)
Explore
(Visualization and basic
description of the data)
Model
(Use variety of statistical and
machine learning models )
Assess
(Evaluate the accuracy and
usefulness of the models)
SEMMA

Data Mining in Cancer Research
1. How can data mining be used for
ultimately curing illnesses like cancer?
2. What do you think are the promises and
major challenges for data miners in
contributing to medical and biological
research endeavors?
Data Mining Methods:
Classification
-learning family
or ordinal) in nature

ctive accuracy
Assessment Methods for
Classification
Accuracy of Classification Models
accuracy estimation is the confusion matrix
True

Positive
Count (TP)
False
Positive
Count (FP)
True
Negative
Count (TN)
False
Negative
Count (FN)
True Class
Positive Negative
P
o
s
it
iv
e
N

e
g
a
ti
v
e
P
re
d
ic
te
d
C
la
s
s
FNTP
TP
RatePositiveTrue
FPTN
TN
RateNegativeTrue

FNFPTNTP
TNTP
Accuracy
FPTP
TP
recision
FNTP
TP
callRe
Estimation Methodologies for
Classification

estimation)
(~70%) and testing (30%)
is split into three sub-sets (training
[~60%], validation [~20%], testing [~20%])
Preprocessed
Data
Training Data
Testing Data
Model
Development
Model
Assessment
(scoring)
2/3
1/3

Classifier
Prediction
Accuracy
Classification
-Fold Cross Validation (rotation estimation)
subsets as training
prediction accuracy training
-one-out, bootstrapping, jackknifing

Classification – ROC Curve
10.90.80.70.60.50.40.30.20.10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1
0.9
0.8
False Positive Rate (1 - Specificity)
T
ru
e
P
o

si
tiv
e
R
a
te
(
S
e
n
si
tiv
ity
) A
B
C
Classification Techniques

-based reasoning
an classifiers
Decision Trees
1. Create a root node and assign all of the training
data to it.
2. Select the best splitting attribute.
3. Add a branch to the root node for each value of
the split. Split the data into mutually exclusive
subsets along the lines of the specific split.
4. Repeat the steps 2 and 3 for each and every leaf
node until the stopping criteria is reached.
A general
algorithm
for
decision
tree
building

division consists of examples from one class
Decision Trees
thms mainly differ on
1. Splitting criteria
2. Stopping criteria
3. Pruning (generalization method)
-pruning versus post-pruning
Decision Trees

class as a result of a decision to branch along
a particular attribute/value
extent of uncertainty or randomness of a
particular attribute/value split
-square statistics (used in CHAID)
2degrees Gets a 1275 Percent Boost in
Churn Identification
1. What does 2degrees do? Why is it important for
2degrees to accurately identify churn?
2. What were the challenges, the proposed solution,
and the obtained results?
3. How can data mining help in identifying customer
churn? How do some companies do it without using
data mining tools and techniques?

4. Why is it important for Delta Lloyd Group to comply
with industry regulations?
Cluster Analysis for Data Mining
natural groupings of things
-learning family
data, then assigns new instances
lts may be used to
classes for targeting/diagnostic purposes

of populations
f problems
for other data mining methods
rare-event detection)
luding both
hierarchical and nonhierarchical), such as k-
means, k-modes, and so on.
[ART], self-organizing map [SOM])
-means algorithm)

it
use of a distance measure to calculate the
closeness between pairs of items.
distance
-Means Clustering Algorithm
-determined number of clusters
Step 1: Randomly generate k random points as initial
cluster centers.
Step 2: Assign each point to the nearest cluster center.
Step 3: Re-compute the new cluster centers.
Repetition step: Repeat steps 3 and 4 until some
convergence criterion is met (usually that the
assignment of points to clusters becomes stable).

Cluster Analysis for Data Mining -
k-Means Clustering Algorithm
Step 1 Step 2 Step 3
Association Rule Mining
between variables (items or events)
ne learning family
ordinary people, such as the famous
“relationship between diapers and beers!”

-of-sale transaction data
“Customer who bought a lap-top computer and
a virus protection software, also bought
extended service plan 70 percent of the time."
from each other!
mining include
-marketing, cross-selling, store
design, catalog design, e-commerce site design,
optimization of online advertising, product pricing,
and sales/promotion configuration
illnesses; diagnosis and patient characteristics and
treatments (to be used in medical DSS); and genes

and their functions (to be used in genomics projects)
X, Y: products and/or services
X: Left-hand-side (LHS)
Y: Right-hand-side (RHS)
S: Support: how often X and Y go together
C: Confidence: how often Y go together with the X
{Extended Service Plan} [30%, 70%]

association rules
-Growth
dentify the frequent
item sets, which are, then converted to
association rules
minimum number of the itemsets
-up approach
(the size of frequent subsets increases from one-
item subsets to two-item subsets, then three-item
subsets, and so on), and
el are tested

against the data for minimum support.
--
Apriori Algorithm
Itemset
(SKUs)
Support
Transaction
No
SKUs
(Item No)
1
1
1
1
1
1

1, 2, 3, 4
2, 3, 4
2, 3
1, 2, 4
1, 2, 3, 4
2, 4
Raw Transaction Data
1
2
3
4
3
6
4
5
Itemset
(SKUs)
Support
1, 2

1, 3
1, 4
2, 3
3
2
3
4
3, 4
5
3
2, 4
Itemset
(SKUs)
Support
1, 2, 4
2, 3, 4
3
3

One-item Itemsets Two-item Itemsets Three-item Itemsets
Data Mining
Software
(formerly Clementine)
- Enterprise Miner
- Intelligent Miner
– Statistica Data
Miner
0 50 100 150 200 250 300
Predixion Software (3)
WordStat (3)

11 Ants Analytics (4)
Teradata Miner (4)
RapidInsight/Veera (5)
Angoss (7)
SAP (BusinessObjects/Sybase/Hana)(7)
XLSTAT (7)
Salford SPM/CART/MARS/TreeNet/RF (9)
Revolution Computing (11)
C4.5/C5.0/See5 (13)
Bayesia (14)
KXEN (14)
Zementis (14)
Stata (15)
IBM Cognos (16)
Miner3D (19)
Mathematica (23)
JMP (32)
Other commercial software (32)

Oracle Data Miner (35)
Tableau (35)
TIBCO Spotfire / S+ / Miner (37)
Other free software (39)
Microsoft SQL Server (40)
Orange (42)
SAS Enterprise Miner (46)
IBM SPSS Modeler (54)
IBM SPSS Statistics (62)
MATLAB (80)
Rapid-I RapidAnalytics (83)
SAS (101)
StatSoft Statistica (112)
Weka / Pentaho (118)
KNIME (174)
Rapid-I RapidMiner (213)
Excel (238)
R (245)

Source: KDNuggets.com
Big Data Software Tools
and Platforms
0 10 20 30 40 50 60 70 80
Other Hadoop-based tools (10)
Other Big Data software (21)
NoSQL databases (33)
Amazon Web Services (AWS) (36)
Apache Hadoop/Hbase/Pig/Hive (67)
0 50 100 150 200 250 300
F# (5)
Awk/Gawk/Shell (31)
Perl (37)
Other languages (57)
C/C++ (66)
Python (119)

Java (138)
SQL (185)
R (245)
Data Mining Goes to Hollywood:
Predicting Financial Success of Movies
oblem
Data Mining Goes to Hollywood!
Independent Variable
Number of

Values
Possible Values
MPAA Rating 5 G, PG, PG-13, R, NR
Competition 3 High, Medium, Low
Star value 3 High, Medium, Low
Genre 10
Sci-Fi, Historic Epic Drama,
Modern Drama, Politically
Related, Thriller, Horror,
Comedy, Cartoon, Action,
Documentary
Special effects 3 High, Medium, Low
Sequel 1 Yes, No
Number of screens 1 Positive integer
Class No. 1 2 3 4 5 6 7 8 9
Range
(in $Millions)
< 1

(Flop)
> 1
< 10
> 10
< 20
> 20
< 40
> 40
< 65
> 65
< 100
> 100
< 150
> 150
< 200
> 200
(Blockbuster)

Dependent
Variable
Independent
Variables
A Typical
Classification
Problem
Model
Development
process
Model
Assessment
process
The DM
Process
Map in
IBM
SPSS
Modeler

Prediction Models
Individual Models Ensemble Models
Performance
Measure SVM ANN C&RT
Random
Forest
Boosted
Tree
Fusion
(Average)
Count (Bingo) 192 182 140 189 187 194
Count (1-Away) 104 120 126 121 104 120
Accuracy (% Bingo) 55.49% 52.60% 40.46% 54.62% 54.05%
56.07%
Accuracy (% 1-Away) 85.55% 87.28% 76.88% 89.60% 84.10%
90.75%

Standard deviation 0.93 0.87 1.05 0.76 0.84 0.63
* Training set: 1998 – 2005 movies; Test set: 2006 movies
Data Mining & Privacy Issues
Predicting Customer Buying Patterns—
The Target Story
1. What do you think about data mining and its
implication for privacy? What is the threshold
between discovery of knowledge and
infringement of privacy?
2. Did Target go too far? Did it do anything
illegal? What do you think Target should have
done? What do you think Target should do
next (quit these types of practices)?
Data Mining Myths

provides instant solutions/predictions
degrees
customer data
ther name for the good-old statistics
Common Data Mining Blunders
1. Selecting the wrong problem for data mining
2. Ignoring what your sponsor thinks data mining
is and what it really can/cannot do
3. Not leaving insufficient time for data
acquisition, selection and preparation
4. Looking only at aggregated results and not at
individual records/predictions
5. Being sloppy about keeping track of the data
mining procedure and results
6. …more in the book

End of the Chapter
All rights reserved. No part of this publication may be
reproduced,
stored in a retrieval system, or transmitted, in any form or by
any
means, electronic, mechanical, photocopying, recording, or
otherwise,
without the prior written permission of the publisher. Printed in
the
United States of America.
Copyright © 2014 Pearson Education, Inc.

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