Chapter 4

Report
Business Intelligence:
A Managerial Perspective on
Analytics (3rd Edition)
Chapter 4:
Data Mining
Learning Objectives
 Define data mining as an enabling technology for
business intelligence
 Understand the objectives and benefits of
business analytics and data mining
 Recognize the wide range of applications of data
mining
 Learn the standardized data mining processes
 CRISP-DM
 SEMMA
 KDD
(Continued…)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 2
Learning Objectives
 Understand the steps involved in data
preprocessing for data mining
 Learn different methods and algorithms of data
mining
 Build awareness of the existing data mining
software tools
 Commercial versus free/open source
 Understand the pitfalls and myths of data mining
Copyright © 2014 Pearson Education, Inc.
Slide 4- 3
Opening Vignette…
Cabela’s Reels in More Customers with
Advanced Analytics and Data Mining
 Decision situation
 Problem
 Proposed solution
 Results
 Answer & discuss the case questions.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 4
Questions for the Opening Vignette
1. Why should retailers, especially omni-channel retailers,
2.
3.
4.
5.
pay extra attention to advanced analytics and data mining?
What are the top challenges for multi-channel retailers?
Can you think of other industry segments that face similar
problems/challenges?
What are the sources of data that retailers such as
Cabela’s use for their data mining projects?
What does it mean to have a “single view of the
customer”? How can it be accomplished?
What type of analytics help did Cabela’s get from their
efforts? Can you think of any other potential benefits of
analytics for large-scale retailers like Cabela’s?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 5
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.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 6
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,…
Copyright © 2014 Pearson Education, Inc.
Slide 4- 7
Data Mining at the Intersection of
Many Disciplines
ial
e
Int
tis
tic
s
c
tifi
Ar
Pattern
Recognition
en
Sta
llig
Mathematical
Modeling
Machine
Learning
ce
DATA
MINING
Databases
Management Science &
Information Systems
Copyright © 2014 Pearson Education, Inc.
Slide 4- 8
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 Webbased 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.).
Copyright © 2014 Pearson Education, Inc.
Slide 4- 9
Application Case 4.1
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?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 10
Data in Data Mining
 Data: a collection of facts usually obtained as the result of
experiences, observations, or experiments.
 Data may consist of numbers, words, images, …
 Data: lowest level of abstraction (from which information and
knowledge are derived).
- DM with different
data types?
Data
Unstructured or
Semi-Structured
Structured
Categorical
Nominal
Ordinal
Numerical
Interval
Textual
Multimedia
- Other data types?
HTML/XML
Ratio
Copyright © 2014 Pearson Education, Inc.
Slide 4- 11
What Does DM Do?
How Does it Work?
 DM extracts 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 12
Application Case 4.2
Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis
Police Department Pinpoint Crime and
Focus Police Resources
Questions for Discussion
1. How did the Memphis Police Department
use data mining to better combat crime?
2. What were the challenges, the proposed
solution, and the obtained results?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 13
A Taxonomy for Data Mining Tasks
Data Mining
Learning Method
Popular Algorithms
Supervised
Classification and Regression Trees,
ANN, SVM, Genetic Algorithms
Classification
Supervised
Decision trees, ANN/MLP, SVM, Rough
sets, Genetic Algorithms
Regression
Supervised
Linear/Nonlinear Regression, Regression
trees, ANN/MLP, SVM
Unsupervised
Apriory, OneR, ZeroR, Eclat
Link analysis
Unsupervised
Expectation Maximization, Apriory
Algorithm, Graph-based Matching
Sequence analysis
Unsupervised
Apriory Algorithm, FP-Growth technique
Unsupervised
K-means, ANN/SOM
Prediction
Association
Clustering
Outlier analysis
Unsupervised
K-means, Expectation Maximization (EM)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 14
Data Mining Tasks
 Time-series forecasting
 Part of sequence or link analysis?
 Visualization
 Another data mining task?
 Types of DM
 Hypothesis-driven data mining
 Discovery-driven data mining
Copyright © 2014 Pearson Education, Inc.
Slide 4- 15
Data Mining Applications
 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 16
Data Mining Applications
 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 17
Data Mining Applications
 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 18
Data Mining Applications
 Computer hardware and software
 Science and engineering
 Government and defense
 Homeland security and law enforcement
 Travel industry
Highly popular application
 Healthcare
areas for data mining
 Medicine
 Entertainment industry
 Sports
 Etc.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 19
Application Case 4.3
A Mine on Terrorist Funding
Questions for Discussion
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?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 20
Data Mining Process
 A manifestation of best practices
 A systematic way to conduct DM projects
 Different groups have different versions
 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)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 21
Data Mining Process
Source: KDNuggets.com
Copyright © 2014 Pearson Education, Inc.
Slide 4- 22
Data Mining Process: CRISP-DM
1
Business
Understanding
2
Data
Understanding
3
Data
Preparation
Data Sources
6
4
Deployment
Model
Building
5
Testing and
Evaluation
Copyright © 2014 Pearson Education, Inc.
Slide 4- 23
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
Accounts for
~85% of total
project time
 The process is highly repetitive and
experimental (DM: art versus science?)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 24
Data Preparation – A Critical DM Task
Real-world
Data
Data Consolidation
·
·
·
Collect data
Select data
Integrate data
Data Cleaning
·
·
·
Impute missing values
Reduce noise in data
Eliminate inconsistencies
Data Transformation
·
·
·
Normalize data
Discretize/aggregate data
Construct new attributes
Data Reduction
·
·
·
Reduce number of variables
Reduce number of cases
Balance skewed data
Well-formed
Data
Copyright © 2014 Pearson Education, Inc.
Slide 4- 25
Data Mining Process: SEMMA
Sample
(Generate a representative
sample of the data)
Assess
Explore
(Evaluate the accuracy and
usefulness of the models)
(Visualization and basic
description of the data)
SEMMA
Model
Modify
(Use variety of statistical and
machine learning models )
(Select variables, transform
variable representations)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 26
Application Case 4.4
Data Mining in Cancer Research
Questions for Discussion
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?
1.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 27
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?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 28
Assessment Methods for Classification
 Predictive accuracy
 Hit rate
 Speed
 Model building; predicting
 Robustness
 Scalability
 Interpretability
 Transparency, explainability
Copyright © 2014 Pearson Education, Inc.
Slide 4- 29
Accuracy of Classification Models
 In classification problems, the primary source for
accuracy estimation is the confusion matrix
Predicted Class
Negative
Positive
True Class
Positive
Negative
True
Positive
Count (TP)
False
Positive
Count (FP)
Accuracy 
TP  TN
TP  TN  FP  FN
True Positive Rate 
TP
TP  FN
True Negative Rate 
False
Negative
Count (FN)
True
Negative
Count (TN)
P recision 
TP
TP  FP
Copyright © 2014 Pearson Education, Inc.
TN
TN  FP
Re call 
TP
TP  FN
Slide 4- 30
Estimation Methodologies for
Classification
 Simple split (or holdout or test sample estimation)
 Split the data into 2 mutually exclusive sets training
(~70%) and testing (30%)
2/3
Training Data
Model
Development
Classifier
Preprocessed
Data
1/3
Testing Data
Model
Assessment
(scoring)
Prediction
Accuracy
 For ANN, the data is split into three sub-sets (training
[~60%], validation [~20%], testing [~20%])
Copyright © 2014 Pearson Education, Inc.
Slide 4- 31
Estimation Methodologies for
Classification
 k-Fold Cross Validation (rotation estimation)
 Split the data into k mutually exclusive subsets
 Use each subset as testing while using the rest of the
subsets as training
 Repeat the experimentation for k times
 Aggregate the test results for true estimation of prediction
accuracy training
 Other estimation methodologies
 Leave-one-out, bootstrapping, jackknifing
 Area under the ROC curve
Copyright © 2014 Pearson Education, Inc.
Slide 4- 32
Estimation Methodologies for
Classification – ROC Curve
1
0.9
True Positive Rate (Sensitivity)
0.8
A
0.7
B
0.6
C
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
0.8
0.9
1
False Positive Rate (1 - Specificity)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 33
Classification Techniques
 Decision tree analysis
 Statistical analysis
 Neural networks
 Support vector machines
 Case-based reasoning
 Bayesian classifiers
 Genetic algorithms
 Rough sets
Copyright © 2014 Pearson Education, Inc.
Slide 4- 34
Decision Trees
 Employs the divide and conquer method
 Recursively divides a training set until each
division consists of examples from one class
A general
algorithm
for
decision
tree
building
1.
2.
3.
4.
Create a root node and assign all of the training
data to it.
Select the best splitting attribute.
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.
Repeat steps 2 and 3 for each and every leaf
node until the stopping criteria is reached.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 35
Decision Trees
 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 36
Decision Trees
 Alternative splitting criteria
 Gini index determines the purity of a specific
class as a result of a decision to branch
along a particular attribute/value
 Used in CART
 Information gain uses entropy to measure
the extent of uncertainty or randomness of a
particular attribute/value split
 Used in ID3, C4.5, C5
 Chi-square statistics (used in CHAID)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 37
Application Case 4.5
2degrees Gets a 1275 Percent Boost in
Churn Identification
Questions for Discussion
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?
1.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 38
Cluster Analysis for Data Mining
 Used for automatic identification of
natural groupings of things
 Part of the machine-learning family
 Employs unsupervised learning
 Learns the clusters of things from past
data, then assigns new instances
 There is not an output variable
 Also known as segmentation
Copyright © 2014 Pearson Education, Inc.
Slide 4- 39
Cluster Analysis for Data Mining
 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)
Copyright © 2014 Pearson Education, Inc.
Slide 4- 40
Cluster Analysis for Data Mining
 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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 41
Cluster Analysis for Data Mining
 How many clusters?
 There is not a “truly optimal” way to calculate it
 Heuristics are often used
 Most cluster analysis methods involve the
use of a distance measure to calculate the
closeness between pairs of items.
 Euclidian versus Manhattan/Rectilinear
distance
Copyright © 2014 Pearson Education, Inc.
Slide 4- 42
Cluster Analysis for Data Mining
 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).
Copyright © 2014 Pearson Education, Inc.
Slide 4- 43
Cluster Analysis for Data Mining k-Means Clustering Algorithm
Step 1
Step 2
Copyright © 2014 Pearson Education, Inc.
Step 3
Slide 4- 44
Association Rule Mining
 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!”
Copyright © 2014 Pearson Education, Inc.
Slide 4- 45
Association Rule Mining
 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!
Copyright © 2014 Pearson Education, Inc.
Slide 4- 46
Association Rule Mining
 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)
 …
Copyright © 2014 Pearson Education, Inc.
Slide 4- 47
Association Rule Mining
 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 goes together with X
Example: {Laptop Computer, Antivirus Software} 
{Extended Service Plan} [30%, 70%]
Copyright © 2014 Pearson Education, Inc.
Slide 4- 48
Association Rule Mining
 Algorithms are available for
generating association rules
 Apriori
 Eclat
 FP-Growth
 + Derivatives and hybrids of the three
 The algorithms help identify the
frequent item sets, which are then
converted to association rules
Copyright © 2014 Pearson Education, Inc.
Slide 4- 49
Association Rule Mining
 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)  -Copyright © 2014 Pearson Education, Inc.
Slide 4- 50
Association Rule Mining
Apriori Algorithm
Raw Transaction Data
One-item Itemsets
Two-item Itemsets
Three-item Itemsets
Transaction
No
SKUs
(Item No)
Itemset
(SKUs)
Support
Itemset
(SKUs)
Support
Itemset
(SKUs)
Support
1
1, 2, 3, 4
1
3
1, 2
3
1, 2, 4
3
1
2, 3, 4
2
6
1, 3
2
2, 3, 4
3
1
2, 3
3
4
1, 4
3
1
1, 2, 4
4
5
2, 3
4
1
1, 2, 3, 4
2, 4
5
1
2, 4
3, 4
3
Copyright © 2014 Pearson Education, Inc.
Slide 4- 51
Artificial Neural Networks
for Data Mining
 Artificial neural networks (ANN or NN) are a brain
metaphor for information processing
 a.k.a. Neural Computing
 Very good at capturing highly complex non-linear
functions!
 Many uses – prediction (regression, classification),
clustering/segmentation
 Many application areas - finance, medicine, marketing,
manufacturing, service operations, information systems, …
Copyright © 2014 Pearson Education, Inc.
Slide 4- 52
Biological NN
Dendrites
Synapse
Synapse
Axon
Axon
Biological
versus
Artificial
Neural
Networks
Neuron
Dendrites
Neuron
Artificial NN
x1
Y1
w1
Inputs
Outputs
x2
.
.
.
xn
w2
Processing
Element (PE)
S 
Weights
f (S )
n

i 1
X iW
i
Y
Transfer
Function
Summation
wn
Biological
Neuron
Dendrites
Axon
Synapse
Slow
Many (109)
Artificial
Node (or PE)
Input
Output
Weight
Fast
Few (102)
Y2
.
.
.
Yn
Elements/Concepts of ANN
 Processing element (PE)
 Information processing
 Network structure
 Feedforward vs. recurrent vs. multi-layer…
 Learning parameters
 Supervised/unsupervised, backpropagation,
learning rate, momentum
 ANN Software – NN shells, integrated modules
in comprehensive DM software, …
Copyright © 2014 Pearson Education, Inc.
Slide 4- 54
Data Mining
Software
 Commercial
 IBM SPSS Modeler
(formerly Clementine)
 SAS - Enterprise Miner
 IBM - Intelligent Miner
 StatSoft – Statistica Data
Miner
 … many more
 Free and/or Open Source
 R
 RapidMiner
 Weka…
R (245)
Excel (238)
Rapid-I RapidMiner (213)
KNIME (174)
Weka / Pentaho (118)
StatSoft Statistica (112)
SAS (101)
Rapid-I RapidAnalytics (83)
MATLAB (80)
IBM SPSS Statistics (62)
IBM SPSS Modeler (54)
SAS Enterprise Miner (46)
Orange (42)
Microsoft SQL Server (40)
Other free software (39)
TIBCO Spotfire / S+ / Miner (37)
Tableau (35)
Oracle Data Miner (35)
Other commercial software (32)
JMP (32)
Mathematica (23)
Miner3D (19)
IBM Cognos (16)
Stata (15)
Zementis (14)
KXEN (14)
Bayesia (14)
C4.5/C5.0/See5 (13)
Revolution Computing (11)
Salford SPM/CART/MARS/TreeNet/RF (9)
XLSTAT (7)
SAP (BusinessObjects/Sybase/Hana)(7)
Angoss (7)
RapidInsight/Veera (5)
Teradata Miner (4)
11 Ants Analytics (4)
WordStat (3)
Predixion Software (3)
0
50
100
150
200
250
Source: KDNuggets.com
Copyright © 2014 Pearson Education, Inc.
Slide 4- 55
300
Big Data Software Tools and Platforms
Apache Hadoop/Hbase/Pig/Hive (67)
Amazon Web Services (AWS) (36)
NoSQL databases (33)
Other Big Data software (21)
Other Hadoop-based tools (10)
R (245)
0
10
20
30
40
SQL
50 (185)
60
70
80
Java (138)
Python (119)
C/C++ (66)
Other languages (57)
Perl (37)
Awk/Gawk/Shell (31)
F# (5)
0
50
Copyright © 2014 Pearson Education, Inc.
100
150
200
250
Slide 4- 56
300
Application Case 4.6
Data Mining Goes to Hollywood:
Predicting Financial Success of Movies
Questions for Discussion
 Decision situation
 Problem
 Proposed solution
 Results
 Answer & discuss the case questions.
Copyright © 2014 Pearson Education, Inc.
Slide 4- 57
Application Case 4.6
Data Mining Goes to Hollywood!
Class No.
Range
(in $Millions)
1
2
3
<1
>1
> 10
(Flop) < 10
< 20
Dependent
Variable
Independent
Variables
A Typical
Classification
Problem
4
5
6
7
8
9
> 20 > 40
> 65
> 100
> 150
> 200
< 40 < 65
< 100
< 150
< 200
(Blockbuster)
Independent Variable
Number of
Possible Values
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
Copyright © 2014 Pearson Education, Inc.
Slide 4- 58
Application Case 4.6
Data Mining Goes to Hollywood!
The DM
Process
Map in
IBM
SPSS
Modeler
Model
Development
process
Model
Assessment
process
Copyright © 2014 Pearson Education, Inc.
Slide 4- 59
Application Case 4.6
Data Mining Goes to Hollywood!
Prediction Models
Individual Models
Performance
Measure
SVM
ANN
Ensemble Models
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%
0.93
0.87
1.05
0.76
0.84
0.63
Standard deviation
* Training set: 1998 – 2005 movies; Test set: 2006 movies
Copyright © 2014 Pearson Education, Inc.
Slide 4- 60
Data Mining Myths
 Data mining …
 provides instant solutions/predictions
 is not yet viable for business applications
 requires a separate, dedicated database
 can only be done by those with advanced
degrees
 is only for large firms that have lots of
customer data
 is another name for the good-old statistics
Copyright © 2014 Pearson Education, Inc.
Slide 4- 61
Common Data Mining Blunders
1.
2.
3.
4.
5.
6.
Selecting the wrong problem for data mining
Ignoring what your sponsor thinks data mining is
and what it really can/cannot do
Not leaving sufficient time for data acquisition,
selection, and preparation
Looking only at aggregated results and not at
individual records/predictions
Being sloppy about keeping track of the data
mining procedure and results
…more in the book
Copyright © 2014 Pearson Education, Inc.
Slide 4- 62
Application Case 4.7
Data Mining & Privacy
Predicting Customer Buying Patterns—
The Target Story
Questions for Discussion
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)?
Copyright © 2014 Pearson Education, Inc.
Slide 4- 63
End of the Chapter
 Questions, comments
Copyright © 2014 Pearson Education, Inc.
Slide 4- 64
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.
Slide 4- 65

similar documents