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Write about 300-400 words in APA format. Please list in-text citation.Describe how DSS/BI technologies and tools can aid in each phase of decision making?
sharda_11e_full_accessible_ppt_04.pptx

sharda_11e_full_accessible_ppt_03.pptx

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Analytics, Data Science and AI:
Systems for Decision Support
Eleventh Edition
Chapter 4
Data Mining Process, Methods, and
Algorithms
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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
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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
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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
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Opening Vignette (2 of 3)
Miami-Dade Police Department Is Using
Predictive Analytics to Foresee and Fight Crime
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?
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Opening Vignette (3 of 3)
Miami-Dade Police Department Is Using
Predictive Analytics to Foresee and Fight Crime
Discussion Questions (continued)
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.
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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.
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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,…
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Data Mining Is a Blend of Multiple
Disciplines
Figure 4.1 Data Mining Is a Blend of Multiple Disciplines.
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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?
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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.)
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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
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Application Case 4.2
American Honda Uses Advanced Analytics to
Improve Warranty Claims
Questions for Discussion:
1. How does American Honda use analytics to improve
warranty claims?
2. In addition to warranty claims, for what other purposes
does American Honda use advanced analytics methods?
3. Can you think of other uses of advanced analytics in the
automotive industry? You can search the Web to find
some answers to this question.
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A Taxonomy for Data Mining
Figure 4.2 A Simple
Taxonomy for Data
Mining Tasks,
Methods, and
Algorithms.
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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?
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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
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Data Mining Applications (2 of 4)
• 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
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Data Mining Applications (3 of 4)
• 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
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Data Mining Applications (4 of 4)
• Computer hardware and software
• Science and engineering
• Government and defense
• Homeland security and law enforcement
• Travel, entertainment, sports
• Healthcare and medicine
• Sports,… virtually everywhere…
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Application Case 4.3
Predictive Analytic and Data Mining Help Stop
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, although essential for fighting
terrorist cells, also jeopardizes individuals’ rights of
privacy?
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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)
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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 steps






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

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Data Mining Process: CRISP-DM
(2 of 2)
• Figure 4.3 The SixStep CRISP-DM Data
Mining Process. →
• The process is highly
repetitive and
experimental (DM: art
versus science?)
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Data Mining Process: SEMMA
Figure 4.5 SEMMA Data Mining Process.
• Developed by SAS Institute
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Data Mining Process: KDD
Figure 4.6 KDD (Knowledge Discovery in Databases)
Process.
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Which Data Mining Process is the
Best?
Figure 4.7 Ranking of Data Mining Methodologies/Processes.
Source: Used with permission from KDnuggets.com.
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Application Case 4.4
Data Mining Helps 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?
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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?
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Assessment Methods for
Classification
• Predictive accuracy
– Hit rate
• Speed
– Model building versus predicting/usage speed
• Robustness
• Scalability
• Interpretability
– Transparency, explainability
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Accuracy of Classification Models
• In classification problems, the primary source for accuracy
estimation is the confusion matrix
Accuracy =
TP + TN
TP + TN + FP + FN
True Positive Rate =
TP
TP + FN
True Negative Rate =
TN
TN + FP
Precision =
TP
TP + FP
Recall =
TP
TP + FN
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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%)
– For Neural Networks, the data is split into three subsets (training [~60%], validation [~20%], testing
[~20%])
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Estimation Methodologies for
Classification: k-Fold Cross
Validation
• 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.
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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)
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Area Under the ROC Curve (AUC)
(1 of 2)
• Works with binary classification
Figure 4.11 A Sample ROC Curve.
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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 good a assessment for skewed class
distributions too!
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Classification Techniques
• Decision tree analysis
• Statistical analysis
• Neural networks
• Support vector machines
• Case-based reasoning
• Bayesian classifiers
• Genetic algorithms
• Rough sets
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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 1. Create a root node and assign all of the training
data to it.
algorithm
(steps) for 2. Select the best splitting attribute.
building a 3. Add a branch to the root node for each value of
decision
the split. Split the data into mutually exclusive
tree
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.
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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
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Ensemble Models for Predictive
Analytics
• Produces more robust and reliable prediction models
Figure 4.12 Graphical Illustration of a Heterogeneous
Ensemble.
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Application Case 4.5
Influence Health Uses Advanced Predictive
Analytics to Focus on the Factors That Really
Influence People’s Healthcare Decisions
Questions for Discussion:
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)?
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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
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Cluster Analysis for Data Mining
(2 of 4)
• 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)
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Cluster Analysis for Data Mining
(3 of 4)
• 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?
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Cluster Analysis for Data Mining
(4 of 4)
• 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).
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Cluster Analysis for Data Mining k-Means Clustering Algorithm
Figure 4.13 A Graphical Illustration of the Steps in the
k-Means Algorithm.
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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!”
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Association Rule Mining (2 of 6)
• 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!
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Association Rule Mining (3 of 6)
• A representative applications of association rule mining
include
– 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)
– …
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Association Rule Mining (4 of 6)
• 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%]
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Association Rule Mining (5 of 6)
• Several algorithms are developed for discovering
(identifying) association rules
– Apriori
– Eclat
– FP-Growth
– + Derivatives and hybrids of the three
• The …
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