Sample A+ Final Exam Essay I function .docx

Sample
A+
Final
Exam
Essay
I
function
in
a
management
capacity
on
a
part-‐time
basis.
I
have
a
human
relations
role
in
both
motivating
members
of
my
organization
and

growing
the
workforce.
As
part
of
a
team,
I
manage
health
benefits.
And,
I
lobby
politicians.
It
is
incumbent
upon
me
to
broaden
my
perspective.
As
such,
I
have
chosen
to
do
so
academically.
A

Culture,
Civilization,
and
Global
Understanding
course
has
assisted
me.
Being
in
an
information
age
makes
it
necessary
to
discern
what
information
is
relevant.
I
have
accessed
a
wealth
of
relevant
data.
In

a
world
of
rapidly
changing
technology,
efforts
must
be
made
to
avoid
stagnation.
I
have
been
exposed
to
innovative
thinking.
And
the
changing
demographics
of
the
planet
exact
a
need
for
understanding
social
change.
The

value
that
comes
from
this
undertaking
serves
to
prepare
me
for
roles
of
increasing
influence.
My
journey
began
with
exposure.
The
Nations,
Cultures,
and
Territorial
Disputes
course
has
exposed
me
to
hard

data
behind
the
news.
Headlines
and
hype
are
readily
available
on
a
home
page
of
the
internet.
But
CQ
Global
Researcher
(course
required
reading)
provides
background
to
world
issues
of
import
to
international
affairs

and
a
global
society.
It
provides
in-‐depth
opinions
from
experts
on
issues
such
as
chemical
weapons
(Mueller,
2013)
and
climate
change
(Green,
2012).
And,
it
provides
a
wealth
of
references.
Whereas
an
internet
search

may
produce
valid
data
on
a
given
topic
according
to
presorted
keywords,
CQ
provides
direct
citations,
applicable
organizations,
and
recommendations
for
further
research.
The
course,
therefore,
serves
to
widen
my
awareness.

From
subsistence
farming
in
Africa
(McKinsey
Global
Institute,
2012)
to
commodities
marketing
in
Latin
America
(Stier,
2012),
from
political
upheaval
in
the
Middle
East
(Hartman,
2012)
to
the
Chinese
challenge
to
capitalism
(McLure,
2012),

and
from
the
closing
of
American
borders
(Karaim,
2013)
to
the
opening
of
the
Arctic
(Weeks,
2013),
cultural
changes
and
their
potential
impact
on
our
way
of
life
are
explored.
This
points
to
the
need

for
adaptability,
innovation,
and
diplomacy
in
a
world
whose
security
cannot
be
relied
upon
to
remain
fixed.
It
presents
opportunities
for
newly
converging
opinions.
This
is
reflected
in
the
emerging
opinions
of
students
preparing

to
enter
the
workforce
in
a
growing
global
economy.
With
a
majority
of
students
believing
climate
change
to
be
the
single
largest
issue
of
our
time
(Rohrmeier,
2015a)
validates
hope
for
an

end
to
fossil
fuel
dependency.
Most
students
feeling
that
it
is
the
public’s
right
to
be
informed
and
involved
in
decisions
concerning
international
trade
treaties
(Rohrmeier,
2015b)
signifies
a
preemptive
strike
against
exploitation
of

affected
workers.
Having
a
student
majority
claim
that
nations
of
the
world
are
obliged
to
participate
in
bringing
peace
to
nations
in
conflict
(Rohrmeier,
2015c)
opens
the
potential
for
negotiations
to
curb
warfare.
But
these

students
must
advocate
for
such
social
changes
in
their
postgraduate
work.
And,
so
must
I.
My
postgraduate
aspirations
are
already
transpiring.
Having
received
a
scholarship
from
the
international
office
of
my
organization

has
placed
me
squarely
under
the
watchful
eyes
of
top
leadership.
I
have
been
working
to
increase
my
influence
among
the
workers
and
leadership
in
the
organization.
I
have
been
seeking
only
modest
advancements
over

the
course
of
the
past
five
years.
I
was
hoping
that
by
2019,
I
would
be
thoroughly
exposed
to,
and
prepared
to
lead
my
office
and
its
jurisdiction
of
eighteen
northern
California
counties.
But,

the
pressure
is
on
for
me
to
take
over
in
July
of
next
year.
Hence,
I
am
in
need
of
a
comprehensive
broadening
of
my
negotiation
skills.
Increasing
in
advocacy
for
social
change
in

my
advancing
roles,
this
course
proved
of
great
assistance
in
my
endeavors.
Effective
negotiations
necessitate
delving
beneath
surface
issues
to
better
understand
positions.
In-‐depth
reporting
and
access
to
pertinent
resources
provided
me
both

practice
and
exposure.
Negotiations
also
require
understanding
the
perspective
of
others.
Open
forum
discussions
offered
me
a
glimpse
into
the
way
people
think.
And,
influencing
negotiations
presents
the
opportunity
to
manage
conflict
and
effect

resolutions
that
exceed
compromise.
Being
that
this
goes
to
the
heart
of
what
I
do,
participation
for
me
was
more
than
just
practice.
Communication
is
a
large
part
of
what
makes
me

successful.
I
give
speeches
and
presentations,
negotiate
and
lobby,
and
participate
in,
coordinate,
and
lead
group
discussions.
This
requires
research
and
exposure
to
a
wide
range
of
social
topics.
Having
your
finger
on
the

pulse
of
segments
of
the
population
plays
no
small
part.
Modern
management
is
fast
becoming
collaborative
leadership.
San
Jose
State’s
Geography
112
(Nations,
Cultures,
and
Territorial
Disputes)
has
aided
me
greatly

in
navigating
my
advancement
in
this
environment.
References
Green,
K.
(2012,
Aug).
Should
the
United
States
adopt
a
Carbon
Tax?
In
Weeks,
J.
(2013,
Jun).
Climate
Change.
CQ
Global
Researcher,

23(22)
(p.537).
Retrieved
from
www.cqresearcher.com
Hartman,
L.
(2012,
Aug).
Islamic
Sectarianism.
CQ
Global
Researcher,
6(15)
(p.353).
Retrieved
from
Karaim,
R.
(2013,
Sep).
Border
Security.
CQ
Global
Researcher,
23(34).

(p.813).
Retrieved
from
McKinsey
Global
Institute
(2012).
Africa
at
Work:
Job
Creation
and
Inclusive
Growth.
In
McLure,
J.
(2012,
Nov).
Booming
Africa.
CQ
Global
Researcher,
6(22)
(p.526).
Retrieved
from

McLure,
J.
(2012,
May).
State
Capitalism.
CQ
Global
Researcher,
6(10)
(p.229).
Retrieved
from
Mueller,
J.
(2013,
Dec).
Does
Use
of
Chemical
Weapons
Warrant
Military
Intervention?
In
Karaim,
R.
(2013,
Dec).

Chemical
and
Biological
Weapons.
CQ
Global
Researcher,
23(44)
(p.1069).
Retrieved
from
Rohrmeier,
K.
(2015a).
Recap:
Large
Scale
Threats
to
Humanity.
Retrieved
from
https://sjsu.instructure.com/courses/1162310/discussion_topics/
2706219
Rohrmeier,
K.
(2015b).

Recap:
U.S.
Trade
&
Globalization.
Retrieved
from
2706237
Rohrmeier,
K.
(2015c).
Recap:
Turmoil
in
the
Middle
East.
Retrieved
from
2706250
Stier,
K.
(2012,
Jun).
China
in

Latin
America.
CQ
Global
Researcher,
6(11)
(p.257).
Retrieved
from
Weeks,
J.
(2013,
Sep).
Future
of
the
Arctic.
CQ
Global
Researcher,
23(33)
(p.789).
Retrieved
from

INTRODUCTION TO DATA
MINING
INTRODUCTION TO DATA
MINING
SECOND EDITION
PANG-NING TAN
Michigan State Universit
MICHAEL STEINBACH
University of Minnesota
ANUJ KARPATNE
VIPIN KUMAR
330 Hudson Street, NY NY 10013
Director, Portfolio Management: Engineering, Computer
Science & Global
Editions: Julian Partridge
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Copyright ©2019 Pearson Education, Inc. All rights reserved.
Manufactured
in the United States of America. This publication is protected
by Copyright,
and permission should be obtained from the publisher prior to
any prohibited
reproduction, storage in a retrieval system, or transmission in
any form or by

any means, electronic, mechanical, photocopying, recording, or
likewise. For
information regarding permissions, request forms and the
appropriate
contacts within the Pearson Education Global Rights &
Permissions
department, please visit www.pearsonhighed.com/permissions/.
Many of the designations by manufacturers and sellers to
distinguish their
products are claimed as trademarks. Where those designations
appear in this
book, and the publisher was aware of a trademark claim, the
designations
have been printed in initial caps or all caps.
Library of Congress Cataloging-in-Publication Data on File
Names: Tan, Pang-Ning, author. | Steinbach, Michael, author. |
Karpatne,
Anuj, author. | Kumar, Vipin, 1956- author.
Title: Introduction to Data Mining / Pang-Ning Tan, Michigan
State
University, Michael Steinbach, University of Minnesota, Anuj
Karpatne,
University of Minnesota, Vipin Kumar, University of
Minnesota.
Description: Second edition. | New York, NY : Pearson
Education, [2019] |
Includes bibliographical references and index.
Identifiers: LCCN 2017048641 | ISBN 9780133128901 | ISBN

0133128903
Subjects: LCSH: Data mining.
Classification: LCC QA76.9.D343 T35 2019 | DDC 006.3/12–
dc23 LC
record available at https://lccn.loc.gov/2017048641
1 18
ISBN-10: 0133128903
ISBN-13: 9780133128901
To our families …
Preface to the Second Edition
Since the first edition, roughly 12 years ago, much has changed
in the field of
data analysis. The volume and variety of data being collected
continues to
increase, as has the rate (velocity) at which it is being collected
and used to
make decisions. Indeed, the term, Big Data, has been used to
refer to the
massive and diverse data sets now available. In addition, the
term data
science has been coined to describe an emerging area that
applies tools and
techniques from various fields, such as data mining, machine
learning,
statistics, and many others, to extract actionable insights from

data, often big
data.
The growth in data has created numerous opportunities for all
areas of data
analysis. The most dramatic developments have been in the area
of predictive
modeling, across a wide range of application domains. For
instance, recent
advances in neural networks, known as deep learning, have
shown impressive
results in a number of challenging areas, such as image
classification, speech
recognition, as well as text categorization and understanding.
While not as
dramatic, other areas, e.g., clustering, association analysis, and
anomaly
detection have also continued to advance. This new edition is in
response to
those advances.
Overview
As with the first edition, the second edition of the book
provides a
comprehensive introduction to data mining and is designed to be
accessible
and useful to students, instructors, researchers, and
professionals. Areas
covered include data preprocessing, predictive modeling,
association
analysis, cluster analysis, anomaly detection, and avoiding false
discoveries.
The goal is to present fundamental concepts and algorithms for
each topic,
thus providing the reader with the necessary background for the
application

of data mining to real problems. As before, classification,
association analysis
and cluster analysis, are each covered in a pair of chapters. The
introductory
chapter covers basic concepts, representative algorithms, and
evaluation
techniques, while the more following chapter discusses
advanced concepts
and algorithms. As before, our objective is to provide the reader
with a sound
understanding of the foundations of data mining, while still
covering many
important advanced topics. Because of this approach, the book
is useful both
as a learning tool and as a reference.
To help readers better understand the concepts that have been
presented, we
provide an extensive set of examples, figures, and exercises.
The solutions to
the original exercises, which are already circulating on the web,
will be made
public. The exercises are mostly unchanged from the last
edition, with the
exception of new exercises in the chapter on avoiding false
discoveries. New
exercises for the other chapters and their solutions will be
available to
instructors via the web. Bibliographic notes are included at the
end of each
chapter for readers who are interested in more advanced topics,
historically
important papers, and recent trends. These have also been

significantly
updated. The book also contains a comprehensive subject and
author index.
What is New in the Second Edition?
Some of the most significant improvements in the text have
been in the two
chapters on classification. The introductory chapter uses the
decision tree
classifier for illustration, but the discussion on many topics—
those that apply
across all classification approaches—has been greatly expanded
and clarified,
including topics such as overfitting, underfitting, the impact of
training size,
model complexity, model selection, and common pitfalls in
model
evaluation. Almost every section of the advanced classification
chapter has
been significantly updated. The material on Bayesian networks,
support
vector machines, and artificial neural networks has been
significantly
expanded. We have added a separate section on deep networks
to address the
current developments in this area. The discussion of evaluation,
which occurs
in the section on imbalanced classes, has also been updated and
improved.
The changes in association analysis are more localized. We have
completely
reworked the section on the evaluation of association patterns
(introductory
chapter), as well as the sections on sequence and graph mining
(advanced

chapter). Changes to cluster analysis are also localized. The
introductory
chapter added the K-means initialization technique and an
updated the
discussion of cluster evaluation. The advanced clustering
chapter adds a new
section on spectral graph clustering. Anomaly detection has
been greatly
revised and expanded. Existing approaches—statistical, nearest
neighbor/density-based, and clustering based—have been
retained and
updated, while new approaches have been added:
reconstruction-based, one-
class classification, and information-theoretic. The
reconstruction-based
approach is illustrated using autoencoder networks that are part
of the deep
learning paradigm. The data chapter has been updated to include
discussions
of mutual information and kernel-based techniques.
The last chapter, which discusses how to avoid false discoveries
and produce
valid results, is completely new, and is novel among other
contemporary
textbooks on data mining. It supplements the discussions in the
other chapters
with a discussion of the statistical concepts (statistical
significance, p-values,
false discovery rate, permutation testing, etc.) relevant to
avoiding spurious
results, and then illustrates these concepts in the context of data
mining

techniques. This chapter addresses the increasing concern over
the validity
and reproducibility of results obtained from data analysis. The
addition of
this last chapter is a recognition of the importance of this topic
and an
acknowledgment that a deeper understanding of this area is
needed for those
analyzing data.
The data exploration chapter has been deleted, as have the
appendices, from
the print edition of the book, but will remain available on the
web. A new
appendix provides a brief discussion of scalability in the
context of big data.
To the Instructor
As a textbook, this book is suitable for a wide range of students
at the
advanced undergraduate or graduate level. Since students come
to this subject
with diverse backgrounds that may not include extensive
knowledge of
statistics or databases, our book requires minimal prerequisites.
No database
knowledge is needed, and we assume only a modest background
in statistics
or mathematics, although such a background will make for
easier going in
some sections. As before, the book, and more specifically, the
chapters
covering major data mining topics, are designed to be as self-

contained as
possible. Thus, the order in which topics can be covered is quite
flexible. The
core material is covered in chapters 2 (data), 3 (classification),
5 (association
analysis), 7 (clustering), and 9 (anomaly detection). We
recommend at least a
cursory coverage of Chapter 10 (Avoiding False Discoveries) to
instill in
students some caution when interpreting the results of their data
analysis.
Although the introductory data chapter (2) should be covered
first, the basic
classification (3), association analysis (5), and clustering
chapters (7), can be
covered in any order. Because of the relationship of anomaly
detection (9) to
classification (3) and clustering (7), these chapters should
precede Chapter 9.
Various topics can be selected from the advanced classification,
association
analysis, and clustering chapters (4, 6, and 8, respectively) to fit
the schedule
and interests of the instructor and students. We also advise that
the lectures
be augmented by projects or practical exercises in data mining.
Although
they are time consuming, such hands-on assignments greatly
enhance the
value of the course.
Support Materials
Support materials available to all readers of this book are
available at
http://www-users.cs.umn.edu/~kumar/dmbook.

PowerPoint lecture slides
Suggestions for student projects
Data mining resources, such as algorithms and data sets
Online tutorials that give step-by-step examples for selected
data mining
techniques described in the book using actual data sets and data
analysis
software
Additional support materials, including solutions to exercises,
are available
only to instructors adopting this textbook for classroom use.
The book’s
resources will be mirrored at www.pearsonhighered.com/cs-
resources.
http://www.pearsonhighered.com/cs-resources
Comments and suggestions, as well as reports of errors, can be
sent to the
authors through [email protected]
Acknowledgments
Many people contributed to the first and second editions of the
book. We
begin by acknowledging our families to whom this book is
dedicated.
Without their patience and support, this project would have
been impossible.
We would like to thank the current and former students of our
data mining
groups at the University of Minnesota and Michigan State for

their
contributions. Eui-Hong (Sam) Han and Mahesh Joshi helped
with the initial
data mining classes. Some of the exercises and presentation
slides that they
created can be found in the book and its accompanying slides.
Students in our
data mining groups who provided comments on drafts of the
book or who
contributed in other ways include Shyam Boriah, Haibin Cheng,
Varun
Chandola, Eric Eilertson, Levent Ertöz, Jing Gao, Rohit Gupta,
Sridhar Iyer,
Jung-Eun Lee, Benjamin Mayer, Aysel Ozgur, Uygar Oztekin,
Gaurav
Pandey, Kashif Riaz, Jerry Scripps, Gyorgy Simon, Hui Xiong,
Jieping Ye,
and Pusheng Zhang. We would also like to thank the students of
our data
mining classes at the University of Minnesota and Michigan
State University
who worked with early drafts of the book and provided
invaluable feedback.
We specifically note the helpful suggestions of Bernardo
Craemer, Arifin
Ruslim, Jamshid Vayghan, and Yu Wei.
Joydeep Ghosh (University of Texas) and Sanjay Ranka
(University of
Florida) class tested early versions of the book. We also
received many useful
suggestions directly from the following UT students: Pankaj
Adhikari, Rajiv
Bhatia, Frederic Bosche, Arindam Chakraborty, Meghana
Deodhar, Chris
Everson, David Gardner, Saad Godil, Todd Hay, Clint Jones,

Ajay Joshi,
Joonsoo Lee, Yue Luo, Anuj Nanavati, Tyler Olsen, Sunyoung
Park, Aashish
Phansalkar, Geoff Prewett, Michael Ryoo, Daryl Shannon, and
Mei Yang.
Ronald Kostoff (ONR) read an early version of the clustering
chapter and
offered numerous suggestions. George Karypis provided
invaluable LATEX
assistance in creating an author index. Irene Moulitsas also
provided
assistance with LATEX and reviewed some of the appendices.
Musetta
Steinbach was very helpful in finding errors in the figures.
We would like to acknowledge our colleagues at the University
of Minnesota
and Michigan State who have helped create a positive
environment for data
mining research. They include Arindam Banerjee, Dan Boley,
Joyce Chai,
Anil Jain, Ravi Janardan, Rong Jin, George Karypis, Claudia
Neuhauser,
Haesun Park, William F. Punch, György Simon, Shashi Shekhar,
and Jaideep
Srivastava. The collaborators on our many data mining projects,
who also
have our gratitude, include Ramesh Agrawal, Maneesh
Bhargava, Steve
Cannon, Alok Choudhary, Imme Ebert-Uphoff, Auroop
Ganguly, Piet C. de
Groen, Fran Hill, Yongdae Kim, Steve Klooster, Kerry Long,

Nihar
Mahapatra, Rama Nemani, Nikunj Oza, Chris Potter, Lisiane
Pruinelli,
Nagiza Samatova, Jonathan Shapiro, Kevin Silverstein, Brian
Van Ness,
Bonnie Westra, Nevin Young, and Zhi-Li Zhang.
The departments of Computer Science and Engineering at the
University of
Minnesota and Michigan State University provided computing
resources and
a supportive environment for this project. ARDA, ARL, ARO,
DOE, NASA,
NOAA, and NSF provided research support for Pang-Ning Tan,
Michael
Stein-bach, Anuj Karpatne, and Vipin Kumar. In particular,
Kamal Abdali,
Mitra Basu, Dick Brackney, Jagdish Chandra, Joe Coughlan,
Michael Coyle,
Stephen Davis, Frederica Darema, Richard Hirsch, Chandrika
Kamath,
Tsengdar Lee, Raju Namburu, N. Radhakrishnan, James
Sidoran, Sylvia
Spengler, Bhavani Thuraisingham, Walt Tiernin, Maria
Zemankova, Aidong
Zhang, and Xiaodong Zhang have been supportive of our
research in data
mining and high-performance computing.
It was a pleasure working with the helpful staff at Pearson
Education. In
particular, we would like to thank Matt Goldstein, Kathy Smith,
Carole
Snyder, and Joyce Wells. We would also like to thank George
Nichols, who
helped with the art work and Paul Anagnostopoulos, who

provided LATEX
support.
We are grateful to the following Pearson reviewers: Leman
Akoglu (Carnegie
Mellon University), Chien-Chung Chan (University of Akron),
Zhengxin
Chen (University of Nebraska at Omaha), Chris Clifton (Purdue
University),
Joy-deep Ghosh (University of Texas, Austin), Nazli Goharian
(Illinois
Institute of Technology), J. Michael Hardin (University of
Alabama), Jingrui
He (Arizona State University), James Hearne (Western
Washington
University), Hillol Kargupta (University of Maryland, Baltimore
County and
Agnik, LLC), Eamonn Keogh (University of California-
Riverside), Bing Liu
(University of Illinois at Chicago), Mariofanna Milanova
(University of
Arkansas at Little Rock), Srinivasan Parthasarathy (Ohio State
University),
Zbigniew W. Ras (University of North Carolina at Charlotte),
Xintao Wu
(University of North Carolina at Charlotte), and Mohammed J.
Zaki
(Rensselaer Polytechnic Institute).
Over the years since the first edition, we have also received
numerous
comments from readers and students who have pointed out typos
and various

other issues. We are unable to mention these individuals by
name, but their
input is much appreciated and has been taken into account for
the second
edition.
Contents
1. Preface to the Second Edition v
1. 1 Introduction 1
1. 1.1 What Is Data Mining? 4
2. 1.2 Motivating Challenges 5
3. 1.3 The Origins of Data Mining 7
4. 1.4 Data Mining Tasks 9
5. 1.5 Scope and Organization of the Book 13
6. 1.6 Bibliographic Notes 15
1. 1.7 Exercises 21
2. 2 Data 23
1. 2.1 Types of Data 26
1. 2.1.1 Attributes and Measurement 27
2. 2.1.2 Types of Data Sets 34
2. 2.2 Data Quality 42

1. 2.2.1 Measurement and Data Collection Issues 42
2. 2.2.2 Issues Related to Applications 49
3. 2.3 Data Preprocessing 50
1. 2.3.1 Aggregation 51
2. 2.3.2 Sampling 52
3. 2.3.3 Dimensionality Reduction 56
4. 2.3.4 Feature Subset Selection 58
5. 2.3.5 Feature Creation 61
6. 2.3.6 Discretization and Binarization 63
7. 2.3.7 Variable Transformation 69
4. 2.4 Measures of Similarity and Dissimilarity 71
1. 2.4.1 Basics 72
2. 2.4.2 Similarity and Dissimilarity between Simple Attributes
74
3. 2.4.3 Dissimilarities between Data Objects 76
4. 2.4.4 Similarities between Data Objects 78
5. 2.4.5 Examples of Proximity Measures 79

6. 2.4.6 Mutual Information 88
7. 2.4.7 Kernel Functions* 90
8. 2.4.8 Bregman Divergence* 94
9. 2.4.9 Issues in Proximity Calculation 96
10. 2.4.10 Selecting the Right Proximity Measure 98
1. 2.6 Exercises 105
3. 3 Classification: Basic Concepts and Techniques 113
1. 3.1 Basic Concepts 114
2. 3.2 General Framework for Classification 117
3. 3.3 Decision Tree Classifier 119
1. 3.3.1 A Basic Algorithm to Build a Decision Tree 121
2. 3.3.2 Methods for Expressing Attribute Test Conditions 124
3. 3.3.3 Measures for Selecting an Attribute Test Condition 127
4. 3.3.4 Algorithm for Decision Tree Induction 136
5. 3.3.5 Example Application: Web Robot Detection 138
6. 3.3.6 Characteristics of Decision Tree Classifiers 140

4. 3.4 Model Overfitting 147
1. 3.4.1 Reasons for Model Overfitting 149
5. 3.5 Model Selection 156
1. 3.5.1 Using a Validation Set 156
2. 3.5.2 Incorporating Model Complexity 157
3. 3.5.3 Estimating Statistical Bounds 162
4. 3.5.4 Model Selection for Decision Trees 162
6. 3.6 Model Evaluation 164
1. 3.6.1 Holdout Method 165
2. 3.6.2 Cross-Validation 165
7. 3.7 Presence of Hyper-parameters 168
1. 3.7.1 Hyper-parameter Selection 168
2. 3.7.2 Nested Cross-Validation 170
8. 3.8 Pitfalls of Model Selection and Evaluation 172
1. 3.8.1 Overlap between Training and Test Sets 172
2. 3.8.2 Use of Validation Error as Generalization Error 172
9. 3.9 Model Comparison* 173

1. 3.9.1 Estimating the Confidence Interval for Accuracy 174
2. 3.9.2 Comparing the Performance of Two Models 175
4. 4 Classification: Alternative Techniques 193
1. 4.1 Types of Classifiers 193
2. 4.2 Rule-Based Classifier 195
1. 4.2.1 How a Rule-Based Classifier Works 197
2. 4.2.2 Properties of a Rule Set 198
3. 4.2.3 Direct Methods for Rule Extraction 199
4. 4.2.4 Indirect Methods for Rule Extraction 204
5. 4.2.5 Characteristics of Rule-Based Classifiers 206
3. 4.3 Nearest Neighbor Classifiers 208
1. 4.3.1 Algorithm 209
2. 4.3.2 Characteristics of Nearest Neighbor Classifiers 210
4. 4.4 Naïve Bayes Classifier 212
1. 4.4.1 Basics of Probability Theory 213

2. 4.4.2 Naïve Bayes Assumption 218
5. 4.5 Bayesian Networks 227
1. 4.5.1 Graphical Representation 227
2. 4.5.2 Inference and Learning 233
3. 4.5.3 Characteristics of Bayesian Networks 242
6. 4.6 Logistic Regression 243
1. 4.6.1 Logistic Regression as a Generalized Linear Model 244
2. 4.6.2 Learning Model Parameters 245
3. 4.6.3 Characteristics of Logistic Regression 248
7. 4.7 Artificial Neural Network (ANN) 249
1. 4.7.1 Perceptron 250
2. 4.7.2 Multi-layer Neural Network 254
3. 4.7.3 Characteristics of ANN 261
8. 4.8 Deep Learning 262
1. 4.8.1 Using Synergistic Loss Functions 263
2. 4.8.2 Using Responsive Activation Functions 266
3. 4.8.3 Regularization 268

4. 4.8.4 Initialization of Model Parameters 271
5. 4.8.5 Characteristics of Deep Learning 275
9. 4.9 Support Vector Machine (SVM) 276
1. 4.9.1 Margin of a Separating Hyperplane 276
2. 4.9.2 Linear SVM 278
3. 4.9.3 Soft-margin SVM 284
4. 4.9.4 Nonlinear SVM 290
5. 4.9.5 Characteristics of SVM 294
10. 4.10 Ensemble Methods 296
1. 4.10.1 Rationale for Ensemble Method 297
2. 4.10.2 Methods for Constructing an Ensemble Classifier 297
3. 4.10.3 Bias-Variance Decomposition 300
4. 4.10.4 Bagging 302
5. 4.10.5 Boosting 305
6. 4.10.6 Random Forests 310
7. 4.10.7 Empirical Comparison among Ensemble Methods 312
11. 4.11 Class Imbalance Problem 313
1. 4.11.1 Building Classifiers with Class Imbalance 314

2. 4.11.2 Evaluating Performance with Class Imbalance 318
3. 4.11.3 Finding an Optimal Score Threshold 322
4. 4.11.4 Aggregate Evaluation of Performance 323
12. 4.12 Multiclass Problem 330
5. 5 Association Analysis: Basic Concepts and Algorithms 357
1. 5.1 Preliminaries 358
2. 5.2 Frequent Itemset Generation 362
1. 5.2.1 The Apriori Principle 363
2. 5.2.2 Frequent Itemset Generation in the Apriori Algorithm
364
3. 5.2.3 Candidate Generation and Pruning 368
4. 5.2.4 Support Counting 373
5. 5.2.5 Computational Complexity 377
3. 5.3 Rule Generation 380
1. 5.3.1 Confidence-Based Pruning 380
2. 5.3.2 Rule Generation in Apriori Algorithm 381

3. 5.3.3 An Example: Congressional Voting Records 382
4. 5.4 Compact Representation of Frequent Itemsets 384
1. 5.4.1 Maximal Frequent Itemsets 384
2. 5.4.2 Closed Itemsets 386
5. 5.5 Alternative Methods for Generating Frequent Itemsets*
389
6. 5.6 FP-Growth Algorithm* 393
1. 5.6.1 FP-Tree Representation 394
2. 5.6.2 Frequent Itemset Generation in FP-Growth Algorithm
397
7. 5.7 Evaluation of Association Patterns 401
1. 5.7.1 Objective Measures of Interestingness 402
2. 5.7.2 Measures beyond Pairs of Binary Variables 414
3. 5.7.3 Simpson’s Paradox 416
8. 5.8 Effect of Skewed Support Distribution 418
6. 6 Association Analysis: Advanced Concepts 451

1. 6.1 Handling Categorical Attributes 451
2. 6.2 Handling Continuous Attributes 454
1. 6.2.1 Discretization-Based Methods 454
2. 6.2.2 Statistics-Based Methods 458
3. 6.2.3 Non-discretization Methods 460
3. 6.3 Handling a Concept Hierarchy 462
4. 6.4 Sequential Patterns 464
1. 6.4.1 Preliminaries 465
2. 6.4.2 Sequential Pattern Discovery 468
3. 6.4.3 Timing Constraints∗ 473
4. 6.4.4 Alternative Counting Schemes∗ 477
5. 6.5 Subgraph Patterns 479
1. 6.5.1 Preliminaries 480
2. 6.5.2 Frequent Subgraph Mining 483
3. 6.5.3 Candidate Generation 487
4. 6.5.4 Candidate Pruning 493
5. 6.5.5 Support Counting 493

6. 6.6 Infrequent Patterns∗ 493
1. 6.6.1 Negative Patterns 494
2. 6.6.2 Negatively Correlated Patterns 495
3. 6.6.3 Comparisons among Infrequent Patterns, Negative
Patterns, and Negatively Correlated Patterns 496
4. 6.6.4 Techniques for Mining Interesting Infrequent Patterns
498
5. 6.6.5 Techniques Based on Mining Negative Patterns 499
6. 6.6.6 Techniques Based on Support Expectation 501
7. 7 Cluster Analysis: Basic Concepts and Algorithms 525
1. 7.1 Overview 528
1. 7.1.1 What Is Cluster Analysis? 528
2. 7.1.2 Different Types of Clusterings 529
3. 7.1.3 Different Types of Clusters 531
2. 7.2 K-means 534
1. 7.2.1 The Basic K-means Algorithm 535

2. 7.2.2 K-means: Additional Issues 544
3. 7.2.3 Bisecting K-means 547
4. 7.2.4 K-means and Different Types of Clusters 548
5. 7.2.5 Strengths and Weaknesses 549
6. 7.2.6 K-means as an Optimization Problem 549
3. 7.3 Agglomerative Hierarchical Clustering 554
1. 7.3.1 Basic Agglomerative Hierarchical Clustering Algorithm
555
2. 7.3.2 Specific Techniques 557
3. 7.3.3 The Lance-Williams Formula for Cluster Proximity 562
4. 7.3.4 Key Issues in Hierarchical Clustering 563
5. 7.3.5 Outliers 564
4. 7.4 DBSCAN 565
1. 7.4.1 Traditional Density: Center-Based Approach 565
2. 7.4.2 The DBSCAN Algorithm 567

5. 7.5 Cluster Evaluation 571
1. 7.5.1 Overview 571
2. 7.5.2 Unsupervised Cluster Evaluation Using Cohesion and
Separation 574
3. 7.5.3 Unsupervised Cluster Evaluation Using the Proximity
Matrix 582
4. 7.5.4 Unsupervised Evaluation of Hierarchical Clustering 585
5. 7.5.5 Determining the Correct Number of Clusters 587
6. 7.5.6 Clustering Tendency 588
7. 7.5.7 Supervised Measures of Cluster Validity 589
8. 7.5.8 Assessing the Significance of Cluster Validity Measures
594
9. 7.5.9 Choosing a Cluster Validity Measure 596
8. 8 Cluster Analysis: Additional Issues and Algorithms 613
1. 8.1 Characteristics of Data, Clusters, and Clustering
Algorithms
614
1. 8.1.1 Example: Comparing K-means and DBSCAN 614
2. 8.1.2 Data Characteristics 615

3. 8.1.3 Cluster Characteristics 617
4. 8.1.4 General Characteristics of Clustering Algorithms 619
2. 8.2 Prototype-Based Clustering 621
1. 8.2.1 Fuzzy Clustering 621
2. 8.2.2 Clustering Using Mixture Models 627
3. 8.2.3 Self-Organizing Maps (SOM) 637
3. 8.3 Density-Based Clustering 644
1. 8.3.1 Grid-Based Clustering 644
2. 8.3.2 Subspace Clustering 648
3. 8.3.3 DENCLUE: A Kernel-Based Scheme for Density-Based
Clustering 652
4. 8.4 Graph-Based Clustering 656
1. 8.4.1 Sparsification 657
2. 8.4.2 Minimum Spanning Tree (MST) Clustering 658
3. 8.4.3 OPOSSUM: Optimal Partitioning of Sparse Similarities
Using METIS 659
4. 8.4.4 Chameleon: Hierarchical Clustering with Dynamic
Modeling 660

5. 8.4.5 Spectral Clustering 666
6. 8.4.6 Shared Nearest Neighbor Similarity 673
7. 8.4.7 The Jarvis-Patrick Clustering Algorithm 676
8. 8.4.8 SNN Density 678
9. 8.4.9 SNN Density-Based Clustering 679
5. 8.5 Scalable Clustering Algorithms 681
1. 8.5.1 Scalability: General Issues and Approaches 681
2. 8.5.2 BIRCH 684
3. 8.5.3 CURE 686
6. 8.6 Which Clustering Algorithm? 690
9. 9 Anomaly Detection 703
1. 9.1 Characteristics of Anomaly Detection Problems 705
1. 9.1.1 A Definition of an Anomaly 705
2. 9.1.2 Nature of Data 706
3. 9.1.3 How Anomaly Detection is Used 707

2. 9.2 Characteristics of Anomaly Detection Methods 708
3. 9.3 Statistical Approaches 710
1. 9.3.1 Using Parametric Models 710
2. 9.3.2 Using Non-parametric Models 714
3. 9.3.3 Modeling Normal and Anomalous Classes 715
4. 9.3.4 Assessing Statistical Significance 717
4. 9.4 Proximity-based Approaches 719
1. 9.4.1 Distance-based Anomaly Score 719
2. 9.4.2 Density-based Anomaly Score 720
3. 9.4.3 Relative Density-based Anomaly Score 722
5. 9.5 Clustering-based Approaches 724
1. 9.5.1 Finding Anomalous Clusters 724
2. 9.5.2 Finding Anomalous Instances 725
6. 9.6 Reconstruction-based Approaches 728

7. 9.7 One-class Classification 732
1. 9.7.1 Use of Kernels 733
2. 9.7.2 The Origin Trick 734
8. 9.8 Information Theoretic Approaches 738
9. 9.9 Evaluation of Anomaly Detection 740
10. 10 Avoiding False Discoveries 755
1. 10.1 Preliminaries: Statistical Testing 756
1. 10.1.1 Significance Testing 756
2. 10.1.2 Hypothesis Testing 761
3. 10.1.3 Multiple Hypothesis Testing 767
4. 10.1.4 Pitfalls in Statistical Testing 776
2. 10.2 Modeling Null and Alternative Distributions 778

1. 10.2.1 Generating Synthetic Data Sets 781
2. 10.2.2 Randomizing Class Labels 782
3. 10.2.3 Resampling Instances 782
4. 10.2.4 Modeling the Distribution of the Test Statistic 783
3. 10.3 Statistical Testing for Classification 783
1. 10.3.1 Evaluating Classification Performance 783
2. 10.3.2 Binary Classification as Multiple Hypothesis Testing
785
3. 10.3.3 Multiple Hypothesis Testing in Model Selection 786
4. 10.4 Statistical Testing for Association Analysis 787
1. 10.4.1 Using Statistical Models 788
2. 10.4.2 Using Randomization Methods 794
5. 10.5 Statistical Testing for Cluster Analysis 795
1. 10.5.1 Generating a Null Distribution for Internal Indices 796
2. 10.5.2 Generating a Null Distribution for External Indices
798
3. 10.5.3 Enrichment 798
6. 10.6 Statistical Testing for Anomaly Detection 800

1. Author Index 816
2. Subject Index 829
3. Copyright Permissions 839
1 Introduction
Rapid advances in data collection and storage technology,
coupled with the
ease with which data can be generated and disseminated, have
triggered the
explosive growth of data, leading to the current age of big data.
Deriving
actionable insights from these large data sets is increasingly
important in
decision making across almost all areas of society, including
business and
industry; science and engineering; medicine and biotechnology;
and
government and individuals. However, the amount of data
(volume), its
complexity (variety), and the rate at which it is being collected
and processed
(velocity) have simply become too great for humans to analyze
unaided.
Thus, there is a great …
Sample

Film
Essay
The
Square
is
a
documentary
that
tracks
the
Egyptian
youth
revolution.
“Egypt
was
without
dignity”,
according
to
Ahmed,
a
revolutionary
youth
highlighted
in
the
documentary
(YouTube,
2015).
They
were
tied
down

by
an
unjust
regime
and
its
dictator.
The
people
were
tortured,
electrocuted,
and
beaten
for
speaking
about
politics.
Young
Ahmed
believed
that
demanding
fundamental
rights
in
the
streets
would
bring
change
to
Egypt.
But,

where
did
these
ideas
originate?
“Freedom”
was
the
chant
from
the
crowd
in
Tahrir
Square.
CNN
covered
the
protest.
They
reported
that
people
feared
leaving
the
Square
due
to
Secret
Police
taking,

and
potentially
killing
them
when
they
returned
home.
They
interviewed
a
young
actor
named
Khalid.
Khalid
claimed
to
be
engaging
the
discourse
of
democracy,
freedom,
social
justice,
and
political
reform.
Before
the
protest
was

over,
U.S.
President
Obama
privately
urged
Egypt’s
President
to
resign
(Jost,
2013).
A
picture
begins
to
emerge.
While
interviewing,
Khalid
continued
(in
tandem
with
his
father)
to
explain
that
the
aspirations
of
the

people
were
not
to
get
rid
of
Mubarak
only,
but
to
establish
a
true
democratic
society
in
Egypt
(YouTube,
2015).
The
film
proceeded
to
provide
footage
of
what
appeared
to
be
scripted
interviews
among

leaders
of
the
movement.
Most
of
the
dialogue
was
presented
in
Egyptian,
with
English
subtitles.
But,
some
dialogue
was
in
English.
In
particular,
there
was
an
attorney
introduced,
who
was
later
identified
as
such

to
gather
evidence.
What
evidence,
however,
is
needed
when
confronting
a
dictatorial
regime?
Freedom
House
is
an
independent
organization
dedicated
to
the
expansion
of
democracy
around
the
world
(Freedom
House,
2015a).
They

advocate
for
U.S.
leadership
and
collaboration
with
like-‐minded
governments
to
vigorously
oppose
dictators.
And,
Human
Rights
Watch
meets
with
governments,
the
United
Nations,
regional
groups,
financial
institutions,
and
corporations
to
press
for
changes
(Human

Rights
Watch,
2015a).
Their
research
is
not
just
about
victims
and
perpetrators,
but
about
determining
who
can
and
should
take
responsibility
for
stopping
rights
violations
and
providing
redress,
the
detailed
and
specific
steps
they

need
to
take,
and
who
else
can
bring
influence
and
leverage
to
bear
(Human
Rights
Watch,
2015b).
It
is
just
these
kind
of
organizations
who
seem
to
be
advising
the
direction
of
the
movement.

The
revolutionary
youth
seem
to
have
been
parroting
something
they
very
recently
learned.
This
became
apparent
in
the
boxed
interviews.
One
young
woman
explained
that
removing
a
national
leader
further
harms
the
people
when

that
leader
is
replaced
with
someone
from
the
same
political
group
(YouTube,
2015).
President
Mubarak
resigned,
but
the
killers
of
protesters
and
those
giving
the
orders
to
kill
were
not
brought
to
trial.
The

Secret
Police
retained
power.
By
the
spring
of
2011,
two
months
since
the
beginning
of
the
revolution
in
Tahrir
Square,
Egypt
remained
under
Emergency
Law.
Since
allowing
the
military
to
talk
them
into
returning

to
their
homes
upon
news
of
the
resignation,
the
revolutionary
youth
determined
to
return
to
the
Square.
Khalid
exclaimed
that
the
main
way
of
communicating
was
through
slogans.
One
came
out
in
song:

Our
revolution
is
a
people’s
revolution.
A
tent
city
was
set
up
in
the
Square.
When
asked
why,
Ahmed
explained
to
fellow
Egyptians
that
it
was
a
mistake
to
leave
before
power
was

in
their
hands.
When
interviewed
by
phone,
Military
Leader
General
Belcheit
explained
that
people’s
demands
should
be
submitted
to
authorities.
They
can
also
complain
to
the
State
media.
Major
Haytham
relegated
protestors
to
stoners,

thieves,
and
thugs.
But,
the
youth
continued
to
lay
claim
to
the
people
being
the
source
of
authority
and
not
the
military.
The
General
personally
delivered
a
curfew
decision
to
those
in
the

square.
The
protestors
were
warned
that
they
would
be
attacked,
if
necessary.
The
exchange
was
recorded
on
video.
The
General
concluded
with
“Off
you
go.
Leave”.
The
military
proceeded
to
clear
the
Square
with

foot
soldiers,
tanks,
and
guns.
Movement
leaders
monitored
the
action
by
computer
screen
from
a
distant
office.
A
young
woman
reported
the
sequence
of
events
to
an
unidentified
party
on
the
other
end
of

a
phone
conversation.
The
tone
of
the
conversation
was
such
that
further
advice
was
expected
as
forthcoming.
The
question
arose,
“can
you
tell
me
the
exact
names?”
The
advice
came
to
be
sure
of
it

before
spreading
the
word.
The
next
move
of
Khalid
was
to
request
digital
video
cameras
for
compiling
footage
to
be
used
at
some
point
as
evidence.
Ahmed
reiterated
by

claiming
that
as
long
as
there
is
a
camera,
the
revolution
will
continue.
When
advised
by
his
father
that
the
media
would
turn
people
from
the
revolution,
Khalid
determined
to
expose
even
the
corrupt
media.

Pan
to
a
billboard
reading
“We,
Your
Egyptian
Armed
Forces
have
Nothing
but
Love
and
Appreciation
for
Egypt’s
Beloved
People
and
It’s
Innocent
Martyrs
of
the
Revolution.”
Arrests
proceeded
nonetheless.
By
the
summer
of

2011,
thousands
had
been
arrested
and
tried.
The
Muslim
Brotherhood
arranged
for
a
gathering
in
the
Square.
Their
chant
was
“The
Quran
is
our
constitution.
Islamic
Rule.
Islamic
rule.”
It
was
thought

that
the
Brotherhood
made
a
deal
with
the
army.
They
were
accused
of
speaking
in
the
name
of
religion
to
get
what
they
want.
The
media
then
blamed
the
revolutionaries
for
destroying
the
country.
But,

the
revolutionary
youth
continued
to
demand
a
new
constitution.
Ahmed
claimed
that
the
Brotherhood
left
them
in
the
Square
to
be
beaten,
arrested,
and
killed
only
after
they
got
what
they
wanted.
What
would

the
revolutions
next
move
be?
Khalid
was
next
found
seeking
the
advice
of
Mona
Anis,
an
activist
having
experience
in
protests
going
back
to
the
1970s.
Khalid
sought
answers
to
the
question

since
the
elections
would
now
provide
two
options:
those
from
the
existing
regime,
and
those
from
the
Brotherhood,
neither
of
which
supported
the
revolutionary
cause.
Mona
advised
moving
forward
with
the
elections,
as
the

alternative
was
continued
military
rule.
This
is
a
clear
indication
that
there
is
an
outside
force
behind
the
movement.
But,
this
was
not
to
say
that
the
movement
was
finished.
While
conducting
a

demonstration
with
demands
to
enter
the
State
media
building,
protestors
were
again
attacked
by
government
forces.
Following
the
deaths
and
injuries,
the
call
went
out
for
all
bodies
to
be
examined,
and
evidence
kept,

with
copies
of
autopsy
reports.
If
this
doesn’t
sound
scripted,
consider
Ragia
Omran,
a
Human
Rights
lawyer
conveniently
arriving
on
the
scene
in
a
timely
manner.
Filmed
autopsies
were
to
provide
evidence
that
deaths
resulted

from
being
run
over
by
tanks.
Movement
leaders
catered
to
her
wishes.
Then,
further
protests
continued.
The
chant
that
prompted
the
next
attack
followed
singing
at
another
rally.
It
went
“The
people
want

the
fall
of
the
military
regime.”
Two
waves
of
foot
soldiers
and
tanks
dispersed
the
crowd
with
sirens,
smoke,
and
ammunition.
Nerve
gas
was
then
thought
to
be
used
at
the
hospital
where
victims

were
being
treated
due
to
the
convulsions
experienced
by
both
patients
and
doctors.
The
big
question
“Did
you
film
any
of
this?”
After
the
battle
had
raged
for
days,
a
video
was
finally
uploaded

to
YouTube.
People
were
outraged
at
the
extent
of
police
brutality.
In
answer,
the
military
claimed
“The
Armed
Forces
will
move
forward
with
Parliamentary
elections.”
One
year
after
the
start
of
the

revolution,
elections
were
held,
and
the
Brotherhood
won
47%
of
the
seats
(Jost,
2013).
The
Presidential
election
followed
in
May
of
2012
(YouTube,
2015).
Brotherhood
Islamist
Mohammed
Morsi
won.
The
revolutionary
youth
were
certain

that
the
people
would
soon
grow
to
hate
these
winners
because
of
the
control
they
would
exercise.
Within
150
days
of
the
Presidential
election,
Morsi
failed
to
meet
promises,
followed
Islamic
Supreme
Leader
dictates,

exceeded
the
privileges
of
predecessor
Mubarak,
and
tailored
the
constitution
to
the
Brotherhood.
It
is
the
constitution
that
the
revolutionary
youth
remained
determined
to
focus
on,
and
it
is
the
constitution
that
came

under
the
direct
scrutiny
of
Human
Rights
activists.
They
claimed
it
vested
too
much
power
in
the
President,
created
significant
exceptions
for
rights,
and
instituted
Islamic
law
at
the
expense
of
religious
freedom
(Jost,

2013).
The
President
was
empowered
to
appoint
the
Prime
Minister,
dissolve
Parliament,
and
name
members
to
the
Supreme
Court.
There
were
significant
qualifications
on
rights,
and
provisions
for
criminal
prosecution
of
those
insulting
the

president,
judiciary,
or
prophets.
Islam
would
be
the
state
religion,
and
the
basis
of
legislation.
More
protesting
followed.
A
grassroots
campaign
compiled
18
million
signatures
to
install
a
transitional
government,
and
hold

early
presidential
elections
(YouTube,
2015).
Millions
protested
Morsi
on
the
1st
anniversary
of
his
election
in
what
was
reported
to
be
perhaps
the
largest
demonstration
in
the
history
of
the
world.
It
became
the

Muslim
Brotherhood
who
would
conduct
a
sit-‐in
at
this
point.
Early
elections
were
announced
by
the
head
of
the
Armed
Forces,
and
the
Head
of
the
Constitutional
Court
would
serve
as

interim
President.
The
Brotherhood
awaited
Army
death
squads.
And,
there
was
a
massacre.
But,
what
of
the
revolutionaries?
In
closing
the
film,
Khalid
is
found
explaining
that
freedom,
social
justice,
and
dignity
cannot

be
achieved
in
two
years.
And,
Ahmed
concludes
that
the
Egyptian
people
want
not
a
leader,
but
a
conscience.
They
are
quite
certain
in
these
assertions
that
are
coincidently
reminiscent
of
the
Civil
Rights

Movement
and
early
Labor
Movement
in
the
U.S.
With
experienced
activists
and
a
human
rights
agenda,
these
assurances
come
with
the
conviction
found
in
mentoring.
And
if
organizations
such
as
Freedom
House
are
receiving

financing
from
the
U.S.
Agency
for
International
Development,
and
the
U.S.
Department
of
State
to
the
tune
of
$250,000
each
since
2013
alone
(Freedom
House,
2015b),
such
mentoring
is
adherent
to
policy.
The
cause

of
democracy
was
left
to
the
people
of
the
region.
And,
the
push
for
political
reform
victimized
the
region
because
of
the
powerful
antidemocratic
power
structures
(Jost,
2013).
References

YouTube
(2015).
The
Square.
Retrieved
from
https://www.youtube.com/watch?v=VzNzOiao41
Jost,
K.
(2013,
Feb).
Unrest
in
the
Arab
World.
CQ
Researcher.
Washington,
DC:
Sage
Publications,
Inc.
Freedom
House
(2015a).
About
Freedom
House.
Retrieved
from
https://www.freedomhouse.org/about-‐us

Freedom
House
(2015b).
Our
Supporters.
Retrieved
from
https://www.freedomhouse.org/content/our-‐supporters
Human
Rights
Watch
(2015a).
About
Our
Research.
Retrieved
from
http://www.hrw.org/about-‐our-‐research
Human
Rights
Watch
(2015b).
Impact.
Retrieved
from
http://www.hrw.org/impact

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