Transcript sharda_dss10_ppt_05_GE

Business Intelligence and Analytics:
Systems for Decision Support
Global Edition
(10th Edition)
Chapter 5:
Data Mining
Learning Objectives
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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
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5-2
CRISP-DM
SEMMA
KDD
(Continued…)
© Pearson Education Limited 2014
Learning Objectives
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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
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5-3
Commercial versus free/open source
Understand the pitfalls and myths of data
mining
© Pearson Education Limited 2014
Opening Vignette…
Cabela’s Reels in More Customers with
Advanced Analytics and Data Mining
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5-4
Decision situation
Problem
Proposed solution
Results
Answer & discuss the case questions.
© Pearson Education Limited 2014
Questions for the
Opening Vignette
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?
1.
5-5
© Pearson Education Limited 2014
Data Mining Concepts/Definitions
Why Data Mining?
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5-6
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.
© Pearson Education Limited 2014
Definition of Data Mining
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5-7
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,…
© Pearson Education Limited 2014
Data Mining is at the Intersection
of Many Disciplines
ial
e
Int
tis
tic
s
c
tifi
Ar
Pattern
Recognition
en
Sta
llig
Mathematical
Modeling
Machine
Learning
Databases
Management Science &
Information Systems
5-8
© Pearson Education Limited 2014
ce
DATA
MINING
Data Mining
Characteristics/Objectives
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5-9
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.).
© Pearson Education Limited 2014
Application Case 5.1
Smarter Insurance: Infinity P&C Improves
Customer Service and Combats Fraud with
Predictive Analytics
Questions for Discussion
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?
1.
5-10
© Pearson Education Limited 2014
Data in Data Mining
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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).
Data
Unstructured or
Semi-Structured
Structured
Categorical
Nominal
5-11
Ordinal
Numerical
Interval
Textual
Ratio
© Pearson Education Limited 2014
Multimedia
HTML/XML
What Does DM Do?
How Does it Work?
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DM extracts patterns from data
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Types of patterns
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5-12
Pattern? A mathematical (numeric and/or
symbolic) relationship among data items
Association
Prediction
Cluster (segmentation)
Sequential (or time series) relationships
© Pearson Education Limited 2014
Application Case 5.2
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?
5-13
© Pearson Education Limited 2014
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
5-14
Unsupervised
K-means, Expectation Maximization (EM)
© Pearson Education Limited 2014
Data Mining Tasks
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Time-series forecasting
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Visualization
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Another data mining task?
Types of DM
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5-15
Part of sequence or link analysis?
Hypothesis-driven data mining
Discovery-driven data mining
© Pearson Education Limited 2014
Data Mining Applications
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Customer Relationship Management
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Banking & Other Financial
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Maximize return on marketing campaigns
Improve customer retention (churn analysis)
Maximize customer value (cross-, up-selling)
Identify and treat most valued customers
Automate the loan application process
Detecting fraudulent transactions
Maximize customer value (cross-, up-selling)
Optimizing cash reserves with forecasting
© Pearson Education Limited 2014
Data Mining Applications
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Retailing and Logistics
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Manufacturing and Maintenance
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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
Predict/prevent machinery failures
Identify anomalies in production systems to optimize
the use manufacturing capacity
Discover novel patterns to improve product quality
© Pearson Education Limited 2014
Data Mining Applications
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Brokerage and Securities Trading
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Insurance
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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
Forecast claim costs for better business planning
Determine optimal rate plans
Optimize marketing to specific customers
Identify and prevent fraudulent claim activities
© Pearson Education Limited 2014
Data Mining Applications
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Computer hardware and software
Science and engineering
Government and defense
Homeland security and law enforcement
Travel industry
Increasingly more
Healthcare
popular application areas
Medicine
for data mining
Entertainment industry
Sports
Etc.
© Pearson Education Limited 2014
Application Case 5.3
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?
5-20
© Pearson Education Limited 2014
Data Mining Process
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A manifestation of best practices
A systematic way to conduct DM projects
Different groups have different versions
Most common standard processes:
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5-21
CRISP-DM (Cross-Industry Standard Process
for Data Mining)
SEMMA (Sample, Explore, Modify, Model, and
Assess)
KDD (Knowledge Discovery in Databases)
© Pearson Education Limited 2014
Data Mining Process
Source: KDNuggets.com
5-22
© Pearson Education Limited 2014
Data Mining Process: CRISP-DM
1
Business
Understanding
2
Data
Understanding
3
Data
Preparation
Data Sources
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4
Deployment
Model
Building
5
Testing and
Evaluation
5-23
© Pearson Education Limited 2014
Data Mining Process: CRISP-DM
Step
Step
Step
Step
Step
Step
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1:
2:
3:
4:
5:
6:
Business Understanding
Data Understanding
Data Preparation (!)
Model Building
Testing and Evaluation
Deployment
Accounts for
~85% of total
project time
The process is highly repetitive and
experimental (DM: art versus science?)
© Pearson Education Limited 2014
Data Preparation – A Critical DM
Task
Real-world
Data
Data Consolidation
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Collect data
Select data
Integrate data
Data Cleaning
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Impute missing values
Reduce noise in data
Eliminate inconsistencies
Data Transformation
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Normalize data
Discretize/aggregate data
Construct new attributes
Data Reduction
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Reduce number of variables
Reduce number of cases
Balance skewed data
Well-formed
Data
5-25
© Pearson Education Limited 2014
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
5-26
Model
Modify
(Use variety of statistical and
machine learning models )
(Select variables, transform
variable representations)
© Pearson Education Limited 2014
Application Case 5.4
Data Mining in Cancer Research
Questions for Discussion
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?
5-27
© Pearson Education Limited 2014
Data Mining Methods:
Classification
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5-28
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?
© Pearson Education Limited 2014
Assessment Methods for
Classification
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Predictive accuracy
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Speed
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Model building; predicting
Robustness
Scalability
Interpretability
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Hit rate
Transparency, explainability
© Pearson Education Limited 2014
Accuracy of Classification Models
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In classification problems, the primary source for
accuracy estimation is the confusion matrix
Predicted Class
Negative
Positive
True Class
Positive
Negative
5-30
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)
Precision 
TP
TP  FP
© Pearson Education Limited 2014
TN
TN  FP
Recall 
TP
TP  FN
Estimation Methodologies for
Classification
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Simple split (or holdout or test sample
estimation)
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Split the data into 2 mutually exclusive sets training
(~70%) and testing (30%)
2/3
Training Data
Classifier
Preprocessed
Data
1/3
Testing Data
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Model
Development
Model
Assessment
(scoring)
For ANN, the data is split into three sub-sets (training
[~60%], validation [~20%], testing [~20%])
5-31
Prediction
Accuracy
© Pearson Education Limited 2014
Estimation Methodologies for
Classification
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k-Fold Cross Validation (rotation estimation)
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Other estimation methodologies
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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
Leave-one-out, bootstrapping, jackknifing
Area under the ROC curve
© Pearson Education Limited 2014
Estimation Methodologies for
Classification – ROC Curve
1
0.9
True Positive Rate (Sensitivity)
0.8
A
0.7
B
0.6
C
0.5
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0.3
0.2
0.1
0
0
0.1
0.2
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0.6
0.7
0.8
False Positive Rate (1 - Specificity)
5-33
© Pearson Education Limited 2014
0.9
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Classification Techniques
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Decision tree analysis
Statistical analysis
Neural networks
Support vector machines
Case-based reasoning
Bayesian classifiers
Genetic algorithms
Rough sets
© Pearson Education Limited 2014
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
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3.
4.
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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.
© Pearson Education Limited 2014
Decision Trees
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DT algorithms mainly differ on
Splitting criteria
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Stopping criteria
2.
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Pre-pruning versus post-pruning
Most popular DT algorithms include
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When to stop building the tree
Pruning (generalization method)
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Which variable, what value, etc.
ID3, C4.5, C5; CART; CHAID; M5
© Pearson Education Limited 2014
Decision Trees
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Alternative splitting criteria
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Gini index determines the purity of a specific
class as a result of a decision to branch along
a particular attribute/value
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Information gain uses entropy to measure the
extent of uncertainty or randomness of a
particular attribute/value split
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Used in CART
Used in ID3, C4.5, C5
Chi-square statistics (used in CHAID)
© Pearson Education Limited 2014
Application Case 5.5
2degrees Gets a 1275 Percent Boost in
Churn Identification
Questions for Discussion
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What does 2degrees do? Why is it important for
2degrees to accurately identify churn?
What were the challenges, the proposed solution,
and the obtained results?
How can data mining help in identifying customer
churn? How do some companies do it without using
data mining tools and techniques?
Why is it important for Delta Lloyd Group to comply
with industry regulations?
© Pearson Education Limited 2014
Cluster Analysis for Data Mining
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5-39
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
© Pearson Education Limited 2014
Cluster Analysis for Data Mining
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Clustering results may be used to
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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)
© Pearson Education Limited 2014
Cluster Analysis for Data Mining
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Analysis methods
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Statistical methods (including both
hierarchical and nonhierarchical), such as kmeans, 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
© Pearson Education Limited 2014
Cluster Analysis for Data Mining
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How many clusters?
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Most cluster analysis methods involve the
use of a distance measure to calculate the
closeness between pairs of items.
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5-42
There is not a “truly optimal” way to calculate
it
Heuristics are often used
Euclidian versus Manhattan/Rectilinear
distance
© Pearson Education Limited 2014
Cluster Analysis for Data Mining
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k-Means Clustering Algorithm
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k : pre-determined number of clusters
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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).
5-43
© Pearson Education Limited 2014
Cluster Analysis for Data Mining k-Means Clustering Algorithm
Step 1
5-44
Step 2
© Pearson Education Limited 2014
Step 3
Association Rule Mining
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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!”
© Pearson Education Limited 2014
Association Rule Mining
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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?
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Put the items next to each other
Promote the items as a package
Place items far apart from each other!
© Pearson Education Limited 2014
Association Rule Mining
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A representative application of association rule
mining includes
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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)
…
© Pearson Education Limited 2014
Association Rule Mining
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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%]
5-48
© Pearson Education Limited 2014
Association Rule Mining
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Algorithms are available for generating
association rules
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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
© Pearson Education Limited 2014
Association Rule Mining
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Apriori Algorithm
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Finds subsets that are common to at least a
minimum number of the itemsets
Uses a bottom-up approach
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5-50
frequent subsets are extended one item at a time
(the size of frequent subsets increases from oneitem 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)  -© Pearson Education Limited 2014
Association Rule Mining
Apriori Algorithm
Raw Transaction Data
5-51
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
© Pearson Education Limited 2014
Data Mining
Software
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Commercial
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IBM SPSS Modeler
(formerly Clementine)
SAS - Enterprise Miner
IBM - Intelligent Miner
StatSoft – Statistica Data
Miner
… many more
Free and/or Open Source
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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
Source: KDNuggets.com
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© Pearson Education Limited 2014
50
100
150
200
250
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
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50
© Pearson Education Limited 2014
100
150
200
250
300
Application Case 5.6
Data Mining Goes to Hollywood:
Predicting Financial Success of Movies
Questions for Discussion
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Decision situation
Problem
Proposed solution
Results
Answer & discuss the case questions.
© Pearson Education Limited 2014
Application Case 5.6
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
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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
© Pearson Education Limited 2014
Application Case 5.6
Data Mining Goes to Hollywood!
The DM
Process
Map in
IBM
SPSS
Modeler
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Model
Development
process
Model
Assessment
process
© Pearson Education Limited 2014
Application Case 5.6
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
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Application Case 5.7
Data Mining & Privacy Issues
Predicting Customer Buying Patterns—
The Target Story
Questions for Discussion
1.
2.
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What do you think about data mining and its
implication for privacy? What is the threshold
between discovery of knowledge and
infringement of privacy?
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)?
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Data Mining Myths

Data mining …
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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
© Pearson Education Limited 2014
Common Data Mining Blunders
1.
2.
3.
4.
5.
6.
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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
© Pearson Education Limited 2014
End of the Chapter
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Questions, comments
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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.
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