3510-6510_Ch4.pptx

Business Intelligence, Analytics, and Data
Science: A Managerial Perspective
Fourth Edition
Chapter 4
Predictive Analytics I: Data
Mining Process, Methods,
and Algorithms
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All Rights Reserved

Learning Objectives (1 of 2)
4.1 Define data mining as an enabling technology for
business analytics
4.2 Understand the objectives and benefits of data mining
4.3 Become familiar with the wide range of applications of
data mining
4.4 Learn the standardized data mining processes
4.5 Learn different methods and algorithms of data mining

Learning Objectives (2 of 2)
4.6 Build awareness of the existing data mining software
tools
4.7 Understand the privacy issues, pitfalls, and myths of
data mining

Opening Vignette (1 of 3)
Miami-Dade Police Department Is Using Predictive
Analytics to Foresee and Fight Crime
• Predictive analytics in law
enforcement
– Policing with less
– New thinking on cold cases
– The big picture starts small
– Success brings credibility
– Just for the facts
– Safer streets for smarter
cities

Discussion Questions
1. Why do law enforcement agencies and departments like
Miami-Dade Police Department embrace advanced
analytics and data mining?
2. What are the top challenges for law enforcement
agencies and departments like Miami-Dade Police
Department? Can you think of other challenges (not
mentioned in this case) that can benefit from data
mining?

3. What are the sources of data that law enforcement
agencies and departments like Miami-Dade Police
Department use for their predictive modeling and data
mining projects?
4. What type of analytics do law enforcement agencies
and departments like Miami-Dade Police Department
use to fight crime?
5. What does “the big picture starts small” mean in this
case? Explain.

Data Mining Concepts and Definitions
Why Data Mining?
• More intense competition at the global scale.
• Recognition of the value in data sources.
• Availability of quality data on customers, vendors, transactions,
Web, etc.
• Consolidation and integration of data repositories into data
warehouses.
• The exponential increase in data processing and storage
capabilities; and decrease in cost.
• Movement toward conversion of information resources into
nonphysical form.

Definition of Data Mining
• The nontrivial process of identifying valid, novel,
potentially useful, and ultimately understandable patterns
in data stored in structured databases.
– Fayyad et al., (1996)
• Keywords in this definition: Process, nontrivial, valid,
novel, potentially useful, understandable.
• Data mining: a misnomer?
• Other names: knowledge extraction, pattern analysis,
knowledge discovery, information harvesting, pattern
searching, data dredging,…

Figure 4.1 Data Mining is a Blend of
Multiple Disciplines

Application Case 4.1
Visa Is Enhancing the Customer Experience While
Reducing Fraud with Predictive Analytics and Data
Mining
Questions for Discussion
1. What challenges were Visa and the rest of the credit card
industry facing?
2. How did Visa improve customer service while also
improving retention of fraud?
3. What is in-memory analytics, and why was it necessary?

Data Mining Characteristics & Objectives
• Source of data for DM is often a consolidated data
warehouse (not always!).
• DM environment is usually a client-server or a Web-
based information systems architecture.
• Data is the most critical ingredient for DM which may
include soft/unstructured data.
• The miner is often an end user.
• Striking it rich requires creative thinking.
• Data mining tools’ capabilities and ease of use are
essential (Web, Parallel processing, etc.).

How Data Mining Works
• DM extract patterns from data
– Pattern? A mathematical (numeric and/or symbolic)
relationship among data items
• Types of patterns
– Association
– Prediction
– Cluster (segmentation)
– Sequential (or time series) relationships

Dell Is Staying Agile and Effective with Analytics in the 21st
Century
1. What was the challenge Dell was facing that led to their
analytics journey?
2. What solution did Dell develop and implement? What were
the results?
3. As an analytics company itself, Dell has used its service
offerings for its own business. Do you think it is easier or
harder for a company to taste its own medicine? Explain.

A Taxonomy for Data Mining
• Figure 4.2 A Simple Taxonomy for Data Mining Tasks,
Methods, and Algorithms
Data Mining Algorithms
K-means, Expectation Maximization (EM)
Autoregressive Methods, Averaging
Methods, Exponential Smoothing, ARIMA
Expectation Maximization, Apriory
Algorithm, Graph-based Matching
Apriory, OneR, ZeroR, Eclat, GA
Linear/Nonlinear Regression, ANN,
Regression Trees, SVM, kNN, GA
Decision Trees, Neural Networks, Support
Vector Machines, kNN, Naïve Bayes, GA
Data Mining Tasks & Methods
Prediction
Classification
Regression
Segmentation
Association
Link analysis
Sequence analysis
Clustering
Apriory Algorithm, FP-Growth, Graph-
based Matching
Time Series
Market-basket
Outlier analysis
Learning Type
K-means, Expectation Maximization (EM)
Supervised
Unsupervised
Supervised
Supervised
Unsupervised
Unsupervised
Unsupervised
Unsupervised

Other Data Mining Patterns/Tasks
• Time-series forecasting
– Part of the sequence or link analysis?
• Visualization
– Another data mining task?
– Covered in Chapter 3
• Data Mining versus Statistics
– Are they the same?
– What is the relationship between the two?

Data Mining Applications (1 of 4)
• Customer Relationship Management
– Maximize return on marketing campaigns
– Improve customer retention (churn analysis)
– Maximize customer value (cross-, up-selling)
– Identify and treat most valued customers
• Banking & Other Financial
– Automate the loan application process
– Detecting fraudulent transactions
– Maximize customer value (cross-, up-selling)
– Optimizing cash reserves with forecasting

• Retailing and Logistics
– Optimize inventory levels at different locations
– Improve the store layout and sales promotions
– Optimize logistics by predicting seasonal effects
– Minimize losses due to limited shelf life
• Manufacturing and Maintenance
– Predict/prevent machinery failures
– Identify anomalies in production systems to optimize
the use manufacturing capacity
– Discover novel patterns to improve product quality

• Brokerage and Securities Trading
– Predict changes on certain bond prices
– Forecast the direction of stock fluctuations
– Assess the effect of events on market movements
– Identify and prevent fraudulent activities in trading
• Insurance
– Forecast claim costs for better business planning
– Determine optimal rate plans
– Optimize marketing to specific customers
– Identify and prevent fraudulent claim activities

• Computer hardware and software
• Science and engineering
• Government and defense
• Homeland security and law enforcement
• Travel, entertainment, sports
• Healthcare and medicine
• Sports,… virtually everywhere…

Predictive Analytic and Data Mining Help Stop 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, although essential for fighting
terrorist cells, also jeopardizes individuals’ rights of
privacy?

Data Mining Process
• A manifestation of the best practices
• A systematic way to conduct DM projects
• Moving from Art to Science for DM project
• Everybody has a different version
• Most common standard processes:
– CRISP-DM (Cross-Industry Standard Process for Data
Mining)
– SEMMA (Sample, Explore, Modify, Model, and Assess)
– KDD (Knowledge Discovery in Databases)

Data Mining Process: CRISP-DM (1 of 2)
• Cross Industry Standard Process for Data Mining
• Proposed in 1990s by a European consortium
• Composed of six consecutive phases
– Step 1: Business Understanding
– Step 2: Data Understanding
– Step 3: Data Preparation
Accounts for
~85% of total
project time
– Step 4: Model Building
– Step 5: Testing and Evaluation
– Step 6: Deployment

Data Mining Process: CRISP-DM (2 of 2)
• Figure 4.3 The Six-Step CRISP-DM Data Mining Process →
• The process is highly repetitive and experimental (DM: art versus science?)
Business
Understanding
Data
Preparation
Model
Building
Testing and
Evaluation
Deployment
Data
Understanding
6
1 2
3
5
4
Data

Data Mining Process: SEMMA
• Figure 4.5 SEMMA Data Mining Process
• Developed by SAS Institute
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)
Feedback

Data Mining Process: KDD
• Figure 4.6 KDD (Knowledge Discovery in Databases) Process
Sources for
Raw Data
Target
Data
Preprocessed
Data
PHASE 5
DEPT 4
DEPT 3
DEPT 2
DEPT 1
PHASE 4
PHASE 3
PHASE 2
PHASE 1
DEPLOYMENT CHART
1 2 3 4 5
Transformed
Data
Extracted
Patterns
Knowledge
“Actionable
Insight”
Data
Selection
Data
Cleaning
Data
Transformation
Data Mining
Internalization
Feedback

Which Data Mining Process is the Best?
• Figure 4.7 Ranking of Data Mining Methodologies/Processes.
0 10 20 30 40 50 60 70
Other methodology (not domain specific)
None
Domain-specific methodology
My organization's
KDD Process
SEMMA
My own
CRISP-DM
Source: Used with permission from KDnuggets.com.

Data Mining Helps 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?
Training and
calibrating the
model
Testing the
model
Artificial Neural
Networks (ANN)
Tabulated Model
Testing Results
(Accuracy, Sensitivity
and Specificity)
Partitioned data
(training &
testing)
Partitioned data
(training &
testing)
Training and
calibrating the
model
Testing the
model
Logistic
Regression (LR)
Training and
calibrating the
model
Testing the
model
Random
Forest (RF)
Assess
variable
importance
Tabulated
Relative Variable
Importance
Results
Data Preprocessing
ü Cleaning
ü Selecting
ü Transforming
Cancer DB 1 Cancer DB 2 Cancer DB n
Combined
Cancer DB
Partitioned data
(training &
testing)

Data Mining Methods: Classification
• Most frequently used DM method
• Part of the machine-learning family
• Employ supervised learning
• Learn from past data, classify new data
• The output variable is categorical (nominal or ordinal) in
nature
• Classification versus regression?
• Classification versus clustering?

Assessment Methods for Classification
• Predictive accuracy
– Hit rate
• Speed
– Model building versus predicting/usage speed
• Robustness
• Scalability
• Interpretability
– Transparency, explainability

Accuracy of Classification Models
• In classification problems, the primary source for accuracy
estimation is the confusion matrix
TP +TN
Accuracy
TP +TN + FP + FN

TP
True PositiveRate =
TP + FN
TN
True NegativeRate =
TN + FP
TP
Precision =
TP + FP
TP
Recall =
TP + FN
True
Positive
Count (TP)
False
Positive
Count (FP)
True
Negative
Count (TN)
False
Negative
Count (FN)
True/Observed Class
Positive Negative
Positive
Negative
Predicted
Class

Estimation Methodologies for
Classification: Single/Simple Split
• Simple split (or holdout or test sample estimation)
– Split the data into 2 mutually exclusive sets: training
(~70%) and testing (30%)
Preprocessed
Data
Training Data
Testing Data
Model
Development
Model
Assessment
(scoring)
2/3
1/3
Trained
Classifier
TP FP
TN
FN
Prediction
Accuracy
– For Neural Networks, the data is split into three sub-
sets (training [~60%], validation [~20%], testing [~20%])

Estimation Methodologies for Classification: k-
Fold Cross Validation (rotation estimation)
• Data is split into k mutual subsets and k number
training/testing experiments are conducted
• Figure 4.10 A Graphical Depiction of k-Fold Cross-
Validation

Additional Estimation Methodologies for
Classification
• Leave-one-out
– Similar to k-fold where k = number of samples
• Bootstrapping
– Random sampling with replacement
• Jackknifing
– Similar to leave-one-out
• Area Under the ROC Curve (AUC)
– ROC: receiver operating characteristics (a term
borrowed from radar image processing)

Area Under the ROC Curve (AUC) (1 of 2)
• Works with binary classification
• Figure 4.11 A Sample ROC Curve

Area Under the ROC Curve (AUC) (2 of 2)
• Produces values from 0
to 1.0
• Random chance is 0.5
and perfect classification
is 1.0
• Produces a good
assessment for skewed
class distributions too! 1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1
0.9
0.8
False Alarms (1 - Specificity)
A
Area Under the
ROC Curve
(AUC) A = 0.84

Classification Techniques
• Decision tree analysis
• Statistical analysis
• Neural networks
• Support vector machines
• Case-based reasoning
• Bayesian classifiers
• Genetic algorithms
• Rough sets

Decision Trees (1 of 2)
• Employs a divide-and-conquer method
• Recursively divides a training set until each division consists of
examples from one class:
A general
algorithm
(steps) for
building a
decision tree
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 steps 2 and 3 for each and every leaf
node until the stopping criteria is reached.

Decision Trees (2 of 2)
• DT algorithms mainly differ on
1. Splitting criteria
▪ Which variable, what value, etc.
2. Stopping criteria
▪ When to stop building the tree
3. Pruning (generalization method)
▪ Pre-pruning versus post-pruning
• Most popular DT algorithms include
– ID3, C4.5, C5; CART; CHAID; M5

Ensemble Models for Predictive Analytics
• Produces more robust and reliable prediction models
• Figure 4.12 Graphical Illustration of a Heterogeneous Ensemble

Influence Health Uses Advanced Predictive Analytics
to Focus on the Factors That Really Influence People’s
Healthcare Decisions
1. What did Influence Health do?
2. What were the challenges, the proposed solutions, and
the obtained results?
3. How can data mining help companies in the healthcare
industry (in ways other than the ones mentioned in this
case)?

Cluster Analysis for Data Mining (1 of 4)
• Used for automatic identification of natural groupings of
things
• Part of the machine-learning family
• Employ unsupervised learning
• Learns the clusters of things from past data, then assigns
new instances
• There is not an output/target variable
• In marketing, it is also known as segmentation

• Clustering results may be used to
– Identify natural groupings of customers
– Identify rules for assigning new cases to classes for
targeting/diagnostic purposes
– Provide characterization, definition, labeling of
populations
– Decrease the size and complexity of problems for
other data mining methods
– Identify outliers in a specific domain (e.g., rare-event
detection)

• Analysis methods
– Statistical methods (including both hierarchical and
nonhierarchical), such as k-means, k-modes, and so
on.
– Neural networks (adaptive resonance theory [ART],
self-organizing map [SOM])
– Fuzzy logic (e.g., fuzzy c-means algorithm)
– Genetic algorithms
• How many clusters?

• k-Means Clustering Algorithm
– k : pre-determined number of clusters
– Algorithm (Step 0: determine value of k)
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
• Figure 4.13 A Graphical Illustration of the Steps in the k-Means
Algorithm
Step 1 Step 2 Step 3

Association Rule Mining (1 of 6)
• A very popular DM method in business
• Finds interesting relationships (affinities) between variables
(items or events)
• Part of machine learning family
• Employs unsupervised learning
• There is no output variable
• Also known as market basket analysis
• Often used as an example to describe DM to ordinary people,
such as the famous “relationship between diapers and beers!”

• Input: the simple point-of-sale transaction data
• Output: Most frequent affinities among items
• Example: according to the transaction data…
“Customer who bought a lap-top computer and a virus
protection software, also bought extended service plan 70
percent of the time.”
• How do you use such a pattern/knowledge?
– Put the items next to each other
– Promote the items as a package
– Place items far apart from each other!

• A representative application of association rule mining
includes
– In business: cross-marketing, cross-selling, store
design, catalog design, e-commerce site design,
optimization of online advertising, product pricing, and
sales/promotion configuration
– In medicine: relationships between symptoms and
illnesses; diagnosis and patient characteristics and
treatments (to be used in medical DSS); and genes
and their functions (to be used in genomics projects)
– …

• Are all association rules interesting and useful?
A Generic Rule: %, %

X Y [S C ]
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
Example: {Laptop Computer, Antivirus Software} 
{Extended Service Plan} [30%, 70%]

• Several algorithms are developed for discovering
(identifying) association rules
– Apriori
– Eclat
– FP-Growth
– + Derivatives and hybrids of the three
• The algorithms help identify the frequent itemsets, which
are then converted to association rules

• Apriori Algorithm
– Finds subsets that are common to at least a minimum
number of the itemsets
– Uses a bottom-up approach
▪ frequent subsets are extended one item at a time
(the size of frequent subsets increases from one-
item subsets to two-item subsets, then three-item
subsets, and so on), and
▪ groups of candidates at each level are tested
against the data for minimum support
(see the figure)  --

Association Rule Mining Apriori Algorithm
• Figure 4.13 A Graphical Illustration of the Steps in the k-Means Algorithm
Itemset
(SKUs)
Support
Transaction
No
SKUs
(Item No)
1001234
1001235
1001236
1001237
1001238
1001239
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 Tools
• Commercial
– IBM SPSS Modeler
(formerly Clementine)
– SAS Enterprise Miner
– Statistica - Dell/Statsoft
– … many more
• Free and/or Open Source
– KNIME
– RapidMiner
– Weka
– R, …
89
89
100
103
121
132
141
147
153
158
161
162
180
193
197
198
210
211
222
225
227
242
263
301
314
315
337
359
462
487
497
521
536
624
641
944
972
1,029
1,325
1,419
0 200 400 600 800 1000 1200 1400 1600
Orange
Gnu Octave
Salford SPM/CART/RF/MARS/TreeNet
Rattle
IBM Watson
Apache Pig
Other Hadoop/HDFS-based tools
Microsoft Azure Machine Learning
QlikView
Hbase
Microsoft Power BI
SAS Enterprise Miner
Scala
H2O
Other programming and data languages
Other free analytics/data mining tools
C/C++
SQL on Hadoop tools
IBM SPSS Modeler
SAS base
Dataiku
IBM SPSS Statistics
MATLAB
Unix shell/awk/gawk
Microsoft SQL Server
Weka
Mllib
Hive
Anaconda
Java
SciKit-Learn
KNIME
Tableau
Spark
Hadoop
RapidMiner
Excel
SQL
Python
R
Legend:
[Orange] Free/Open Source tools
[Green] Commercial tools
[Blue] Hadoop/Big Data tools

Application Case 4.6 (1 of 5)
Data Mining Goes to Hollywood: Predicting Financial
Success of Movies
• Goal: Predicting financial success of Hollywood movies
before the start of their production process
• How: Use of advanced predictive analytics methods
• Results: promising

A Typical Classification Problem
Dependent Variable
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)

Independent Variables
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 2 Yes, No
Number of screens 1 Positive integer

The DM Process Map in IBM SPSS Modeler
Model
Development
process
Model
Assessment
process

*Training set 1998 – 2005 movies; Test set : 2006 Movies

Table 4.6 Data Mining Myths
Myth Reality
Data mining provides instant, crystal-ball-like
predictions.
Data mining is a multistep process that requires
deliberate, proactive design and use.
Data mining is not yet viable for mainstream
business applications.
The current state of the art is ready to go for
almost any business type and/or size.
Data mining requires a separate, dedicated
database.
Because of the advances in database technology,
a dedicated database is not required.
Only those with advanced degrees can do data
mining.
Newer Web-based tools enable managers of all
educational levels to do data mining.
Data mining is only for large firms that have lots of
customer data.
If the data accurately reflect the business or its
customers, any company can use data mining.

Data Mining Mistakes
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. Beginning without the end in mind
4. Not leaving sufficient time for data acquisition, selection,
and preparation
5. Looking only at aggregated results and not at individual
records/predictions
6. … 10 more mistakes… in your book

End of Chapter 4
• Questions / Comments

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