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# UCM Advanced Analytics Theory and Methods Worksheet

Advanced Analytics – Theory and Methods
Module 4: Analytics Theory/Methods
1
Module 4: Analytics Theory/Methods
1
Advanced Analytics – Theory and Methods
Upon completion of this module, you should be able to:
• Examine analytic needs and select an appropriate technique based on
business objectives; initial hypotheses; and the data’s structure and volume
• Apply some of the more commonly used methods in Analytics solutions
• Explain the algorithms and the technical foundations for the commonly used
methods
• Explain the environment (use case) in which each technique can provide the
most value
• Use appropriate diagnostic methods to validate the models created
• Use R and in-database analytical functions to fit, score and evaluate models
Module 4: Analytics Theory/Methods
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The objectives of this module are listed. The Analytical methods covered are:
Categorization (un-supervised) :
1.K-means clustering
2. Association Rules
Regression
3. Linear
4. Logistic
Classification (supervised)
5.Naïve Bayesian classifier
6. Decision Trees
7. Time Series Analysis
8. Text Analysis
Module 4: Analytics Theory/Methods
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Where “R” we?
• In Module 3 we reviewed R skills and basic statistics
• You can use R to:
 Generate summary statistics to investigate a data set
 Visualize Data
 Perform statistical tests to analyze data and evaluate models
• Now that you have data, and you can see it, you need to plan the
analytic model and determine the analytic method to be used
Module 4: Analytics Theory/Methods
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Module 4 focuses on the most commonly used analytic methods, detailing:
a) Prominent use cases for the method
b) Algorithms to implement the method
c) Diagnostics that are most commonly used to evaluate the effectiveness of the method
d) The Reasons to Choose (+) and Cautions (-) (where the method is most and least effective)
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Applying the Data Analytics Lifecycle
Discovery
Operationalize
Data Prep
• In a typical Data Analytics Problem – you would have gone

through:
Communicate
Model
Results
Planning
• Phase
1 – Discovery – have the problem framed
• Phase 2 – Data Preparation – have the data prepared
Model and determine the method to
Now you need to plan the model
Building
be used.
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Here we recall phases of analytic life cycle we would have gone through before we plan for the
analytic method we should be using with the data.
Module 4: Analytics Theory/Methods
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Phase 3 – Model Planning
Discovery
How Operationalize
do people generally solve this
problem with the kind of data and
resources I have?
Communicate
• Does
that work well enough? Or do I have
Results
to come
up with something new?
• What are related or analogous problems?
Model
How are they solved? Can I do that?
Building
Is the model robust
enough? Have we
failed for sure?
Data Prep
Model
Planning
Do I have a good idea
to try? Can I refine the
analytic plan?
Module 4: Analytics Theory/Methods
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Model planning is the process of determining the appropriate analytic method based on the
problem. It also depends on the type of data and the computational resources available.
Module 4: Analytics Theory/Methods
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What Kind of Problem do I Need to Solve?
How do I Solve it?
The Problem to Solve
The Category of
Techniques
Covered in this Course
I want to group items by similarity.
I want to find structure (commonalities)
in the data
Clustering
K-means clustering
I want to discover relationships between
actions or items
Association Rules
Apriori
I want to determine the relationship
between the outcome and the input
variables
Regression
Linear Regression
Logistic Regression
I want to assign (known) labels to
objects
Classification
Naïve Bayes
Decision Trees
I want to find the structure in a temporal
process
I want to forecast the behavior of a
temporal process
Time Series Analysis
ACF, PACF, ARIMA
I want to analyze my text data
Text Analysis
Regular expressions, Document
representation (Bag of Words), TFIDF
Module 4: Analytics Theory/Methods
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This table lists the typical business questions (column 1) addressed by a category of techniques or
analytical methods (column 2)
Some of the typical business questions for different category of techniques are listed below:
Clustering
images by
Association Rules
Regression
the
Classification
Time Series Analysis
be
Text Analysis
How do I group these documents by topic? How do I group these
What do other people like this person tend to like/buy/watch?
I want to predict the lifetime value of this customer. I want to predict
probability that this loan will default.
Where in the catalog should I place this product? Is this email spam?
What is the likely future price of this stock? What will my sales volume
next month?
Is this a positive product review or a negative one?
As it can be observed that these category of techniques overlap with each other with the type of
problem they can be used to solve.
Questions such as “How do I group these documents?” and “Is this email spam?” , “Is this a positive
product review” can all be answered with a “classification”. But these questions can also be considered
as a Text analysis problem which we cover in this module. Text analysis is defined as term for the
specific process of representing, manipulating, and predicting or learning over text. The tasks
themselves can often be classified as clustering, or classification.
Similarly more than one method can be used to solve the same problem. For example Time Series
Analysis can be used to predict prices over time. Time series is used in cases where the past is
observable to the participants, which is often true of stock, and real estate. Sometimes we can use
regression methods as well. However, regression is most effective when assigning effects to
complicated patterns of treatment.
Column 3 in the table above lists the specific analytical methods that are detailed in the subsequent
lessons in this module.
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Why These Example Techniques?
• Most popular, frequently used:
 Provide the foundation for Data
Science skills on which to build
• Relatively easy for new Data
Scientists to understand &
comprehend
• Applicable to a broad range of
problems in several verticals
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We present in this module K-means clustering, Apriori algorithm for Association rules, Linear
and logistic regression, Classification methods with Naïve Bayesian method and Decision Trees,
Time Series Analysis with Box-Jenkins ARIMA modeling and key concepts such as TF-IDF.
Regular expressions and document representation methods with “bag of words” are chosen to
be presented in this module among several techniques available for the Data Scientists to use
to solve analytic problems. The reasons for which these techniques are chosen among all the
available techniques are listed on this slide.
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Module 4: Advanced Analytics – Theory and Methods
Lesson 1: K-means Clustering
During this lesson the following topics are covered:
• Clustering – Unsupervised learning method
• K-means clustering:

Use cases

The algorithm

Determining the optimum value for K

Diagnostics to evaluate the effectiveness of the method

Reasons to Choose (+) and Cautions (-) of the method
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This lesson covers K-means clustering with these topics.
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Clustering
How do I group these documents by topic?
How do I group my customers by purchase patterns?
• Sort items into groups by similarity:
 Items in a cluster are more similar to each other than they are to
items in other clusters.
 Need to detail the properties that characterize “similarity”
 Or of distance, the “inverse” of similarity
• Not a predictive method; finds similarities, relationships
• Our Example: K-means Clustering
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In machine learning, “unsupervised” refers to the problem of finding a hidden structure within
unlabeled data. In this lesson and the following lesson we will be discussing two unsupervised
learning methods clustering and Association Rules.
Clustering is a popular method used to form homogenous groups within a data set based on
their internal structure. Clustering is a method often used for exploratory analysis of the data.
There are no ”predictions” of any values done with clustering just finding the similarity
between the data and grouping them into clusters
The notion of similarities can be explained with the following examples:
Consider questions such as
1. How do I group these documents by topic?
2. How do I perform customer segmentation to allow for targeted or special marketing
programs.
The definition of “similarity” is specific to the problem domain. We are defining similarity as
those data points with the same “topic” tag or customers who can be profiled in to a same
“age group/income/gender” or a “purchase pattern”.
If we have a vector of measurements of an attribute of the data, the data points that are
grouped into a cluster will have values for the measurement close to each other than to those
data points grouped in a different cluster. In other words the distance, (an inverse of similarity)
between the points within a cluster are always lower than the distance between points in a
different cluster. In a cluster we end up with a tight group (homogeneous) of data points that
are far apart from those data points that end up in a different cluster.
There are many clustering techniques and we are going to discuss one of the most popular
clustering method known as “K-means clustering” in this lesson.
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K-Means Clustering – What is it?
• Used for clustering numerical data, usually a set of

Input: numerical. There must be a distance metric defined over
the variable space.
 Euclidian distance
• Output: The centers of each discovered cluster, and the
assignment of each input datum to a cluster.
 Centroid
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K-means clustering is used to cluster numerical data.
In K-means we define two measures of distances, between two data points(records) and the distance
between two clusters. Distance can be measured (calculated) in a number of ways but four principles
tend to hold true.
1. Distance is not negative (it is stated as an absolute value)
2. Distance from one record to itself is zero.
3. Distance from record I to record J is the same as the distance from record J to record I, again since
the distance is stated as an absolute value, the starting and end points can be reversed.
4. Distance between two records can not be greater than the sum of the distance between each
record and a third record.
Euclidean distance is the most popular method for calculating distance. Euclidian distance is a
“ordinary” distance that one could measure with a ruler. In a single dimension the Euclidian distance is
the absolute value of the differences between two points. The straight line distance between two
points. In a plane with p1 at (x1, y1) and p2 at (x2, y2), it is √((x1 – x2)² + (y1 – y2)²).
In N dimensions, the Euclidean distance between two points p and q is √(∑i=1N (pi-qi)²) where pi (or qi) is
the coordinate of p (or q) in dimension i.
Though there are many other distance measures, the Euclidian distance is the most commonly used
distance measure and many packages use this measure.
The Euclidian distance is influenced by the scale of the variables. Changing the scale (for example from
feet to inches) can significantly influence the results.Second, the equation ignores the relationship
between variables. Lastly, the clustering algorithm is sensitive to outliers. If the data has outliers and
removal of them is not possible, the results of the clustering can be substantially distorted.
The centroid is the center of the discovered cluster. K-means clustering provides this as an output.
When the number of clusters is fixed to k, K-means clustering gives a formal definition as an
optimization problem: find the k cluster centers and assign the objects to the nearest cluster center,
such that the squared distances from the cluster are minimized.
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Use Cases
• Often an exploratory technique:
 Discover structure in the data
 Summarize the properties of each cluster
• Sometimes a prelude to classification:
 “Discovering the classes“
• Examples
 The height, weight and average lifespan of animals
 Household income, yearly purchase amount in dollars, number of
household members of customer households
 Patient record with measures of BMI, HBA1C, HDL
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K-means clustering is often used as a lead-in to classification. It is primarily an
exploratory technique to discover the structure of the data that you might not have
notice before and as a prelude to more focused analysis or decision processes.
Some examples of the set of measurements based on which clustering can be
performed are detailed in the slide.
In the patient record where we have measures such as BMI, HBA1C, HDL with which we
could cluster patients into groups that define varying degrees of risk of a heart disease.
In Classification the labels are known. Whereas in clustering the labels are not known.
Hence clustering can be used to determine the structure in the data and summarize the
properties of each cluster in terms of the measured centroids for the group. The
clusters can define what the initial classes could be.
In low dimensions we can visualize the clusters. It gets very hard to visualize as the
dimensions increase.
There are a lot of applications of the K-mean clustering, examples include pattern
recognition, classification analysis, artificial intelligence, image processing, machine
vision, etc.
In principle, you have several objects and each object has several attributes. You want
to classify the objects based on the attributes, then you can apply this algorithm. For
Data Scientists, K-means is an excellent tool to understand the structure of data and
validate some of the assumptions that are provided by the domain experts pertaining
to the data. We will look into a specific use-case in the following slide.
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Use-Case Example – On-line Retailer
Module 4: Analytics Theory/Methods 12
Here we present a fabricated example of an on-line retailer. The unique selling point of this retailer is that they
make the “returns” simple with an assumption that this policy encourages use and “frequent customers are more
valuable”. So let us validate this assumption.
We took a sample set of customers clustered on purchase frequency, return rate, and lifetime customer value
(LTV).
We define purchase frequency as the number of visits a customer made in a month on average that had a
shopping cart transaction.
We can easily see that return rate has an important effect on customer value.
We clustered the customers into 4 groups, and the plotted 3 graphs taking two of the attributes in a graph. The
data points are represented in the graphs by different colors for each cluster and larger “dot” represents the
centroid for the group.
The groups can be defined broadly as follows:
GP1: Visit less frequently, low return rate, moderate LTV(ranked 3rd)
GP2: Visit often, return a lot of their purchases. Lowest avg LTV (counter intuitive)
GP3: Visit often, return things moderately, High LTV (ranked 2nd) (happy medium)
GP4: Visit rarely, don’t return purchases. Highest avg LTV
It appears that GP3 is the ideal group – they visit often, return things moderately, and are high value. The next
questions are
– Why is it that GP3 is ideal?
– What are the people in these different groups buying?
– Is that affecting LTV?
– Can we raise the LTV of our frequent customers, perhaps by lowering the cost of returns, or by somehow
discouraging customers who return goods too frequently?
– Can we encourage GP4 customers to visit more (without lowering their LTV?)
– Are more frequent customers more valuable?
You can see the range of questions that a Data Scientist can address with the initial analysis with k-means
clustering.
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The Algorithm
1. Choose K; then select K
random “centroids”
In our example, K=3
2. Assign records to the
cluster with the closest
centroid
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Step 1 – K-means clustering begins with the data set segmented into K clusters.
Step 2- Observations are moved from cluster to cluster to help reduce the distance from the
observation to the cluster centroid.
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The Algorithm (Continued)
3. Recalculate the resulting
centroids
Centroid: the mean value of all
the records in the cluster
4. Repeat steps 2 & 3 until record
assignments no longer change
Model Output:
• The final cluster centers
• The final cluster assignments of
the training data
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Step 3 – When observations are moved to a new cluster, the centroid for the affected clusters
needs to be recalculated.
Step 4 – This movement and recalculation is repeated until movement no longer results in an
improvement.
The model output is the final cluster centers and the final cluster assignments for the data.
Selecting the appropriate number of clusters, K, can be done upfront if you possess some
knowledge on what the right number may be. Alternatively you can try the exercise with
different values for K and decide which clusters best suit your needs. Since it is rare that the
appropriate number of clusters in a dataset is known, it is good practice to select a few values
for k and compare the results.
The first partitioning should be done with the same knowledge used to select the appropriate
value of K, for example domain knowledge about the market or industries.
If K was selected without external knowledge, the partitioning can be done without any inputs.
Once all observations are assigned to their closest cluster, the clusters can be evaluated for
their “in-cluster dispersion.” Clusters with the smallest average distance are the most
homogenous. We can also examine the distance between clusters and decide if it makes sense
to combine clusters which may be located close together. We can also use the distance
between clusters to assess how successful the clustering exercise has been. Ideally, the
clusters should not be located close together as the clusters should be well separated.
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Picking K
Heuristic: find the “elbow” of the within-sum-of-squares (wss) plot
as a function of K.
K: # of clusters
ni: # points in ith cluster
ci: centroid of ith cluster
xij: jth point of ith cluster
“Elbows” at k=2,4,6
Module 4: Analytics Theory/Methods 15
Practically based on the domain knowledge, a value for K is picked and the centroids are
computed. Then a different K is chosen and the model is repeated to observe if it enhanced the
cohesiveness of the data points within the cluster group. However if there is no apparent
structure in the data we may have to try multiple values for K. It is an exploratory process.
We present here one of the heuristic approaches used for picking the optimal “K” for the given
dataset. “Within Sum of Squares” – WSS is a measure of how tight on average each cluster is.
For k=1, WSS can be considered the overall dispersion of the data. WSS primarily is a measure
of homogeneity. In general more clusters result in tighter clusters. But having too many clusters
is over-fitting. The formula that defines WSS is shown. The graph depicts the value of WSS on
the Y-axis and the number of clusters on the X-axis. The online retailer example data we
reviewed earlier is the data with which the graph shown here is generated. We repeated the
clustering for 12 different values .When we went from one cluster to two there is a significant
drop in the value of WSS, since with two clusters you get more homogeneity. We look for the
elbow of the curve which provides the optimal number of clusters for the given data.
Visualizing the data helps in confirming the optimal number of clusters. Reviewing the three
pair-wise graphs we plotted for the online retailer example earlier you can see that having four
groups sufficiently explained the data and from the graph above we can also see the elbow of
the curve is at 4.
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Diagnostics – Evaluating the Model
• Do the clusters look separated in at least some of the plots when
you do pair-wise plots of the clusters?
 Pair-wise plots can be used when there are not many variables
• Do you have any clusters with few data points?
 Try decreasing the value of K
• Are there splits on variables that you would expect, but don’t
see?
 Try increasing the value K
• Do any of the centroids seem too close to each other?
 Try decreasing the value of K
Module 4: Analytics Theory/Methods 16
How do we know that we have good clusters?
Pair-wise plots of the clusters provide a good visual confirmation that the clusters are
homogeneous. When the dimensions of the data are not significantly large this method helps
in determining the optimal number of clusters. With these plots you should be able to
determine if the clusters look separated in at least some of the plots. They won’t be very
separated in all of the plots. This can be seen even with the on-line retailer example we saw
earlier. Some of the clusters get mixed in together in some dimensions.
If you feel that your clusters are too small it indicates that you have a large value for K and K
needs to be reduced (try a smaller K). It may be the outliers in the data that tend to cluster
into clusters with less data points.
Alternatively if you see there are splits that you expected but are not seen in the clusters, for
example you expect two different income groups and you don’t see them, you should try a
bigger value for K.
If the centroids seem too close to each other then you should try decreasing the value of K.
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K-Means Clustering – Reasons to Choose (+) and
Cautions (-)
Reasons to Choose (+)
Cautions (-)
Easy to implement
Easy to assign new data to existing
clusters
Which is the nearest cluster center?
Concise output
Coordinates the K cluster centers
Doesn’t handle categorical variables
Sensitive to initialization (first guess)
Variables should all be measured on
similar or compatible scales
Not scale-invariant!
K (the number of clusters) must be
known or decided a priori
Wrong guess: possibly poor results
Tends to produce “round” equi-sized
clusters.
Not always desirable
Module 4: Analytics Theory/Methods 17
K-means clustering is easy to implement and it produces concise output. It is easy to assign
new data to the existing clusters by determining which centroid the new data point is closest to
it.
However K-means works only on the numerical data and does not handle categorical variables.
It is sensitive to the initial guess on the centroids. It is important that the variables must be all
measured on similar or compatible scales. If you measure the living space of a house in square
feet, the cost of the house in thousands of dollars (that is, 1 unit is \$1000), and then you
change the cost of the house to dollars (so one unit is \$1), then the clusters may change. K
should be decided ahead of the modeling process. Wrong guesses for K may lead to improper
clustering.
K-means tends to produce rounded and equal sized clusters. If you have clusters which are
elongated or crescent shaped, K-means may not be able to find these clusters appropriately.
The data in this case may have to be transformed before modeling.
Module 4: Analytics Theory/Methods
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1. Why do we consider K-means clustering as a unsupervised
machine learning algorithm?
2. How do you use “pair-wise” plots to evaluate the effectiveness
of the clustering?
3. Detail the four steps in the K-means clustering algorithm.
4. How do we use WSS to pick the value of K?
5. What is the most common measure of distance used with Kmeans clustering algorithms?
6. The attributes of a data set are “purchase decision (Yes/No),
Gender (M/F), income group (50K). Can you
use K-means to cluster this data set?
Module 4: Analytics Theory/Methods 18
Module 4: Analytics Theory/Methods
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Module 4: Advanced Analytics – Theory and Methods
Lesson 1: K-means Clustering – Summary
During this lesson the following topics were covered:
• Clustering – Unsupervised learning method
• What is K-means clustering
• Use cases with K-means clustering
• The K-means clustering algorithm
• Determining the optimum value for K
• Diagnostics to evaluate the effectiveness of K-means clustering
• Reasons to Choose (+) and Cautions (-) of K-means clustering
Module 4: Analytics Theory/Methods 19
Summary of key-topics presented in this lesson are listed. Take a moment to review them.
Module 4: Analytics Theory/Methods
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