Description
Department of Accounting and Management Information Systems
1
The Ohio State University
The Max M. Fisher College of Business
Department of Accounting and Management Information Systems
AMIS 7640 – Data Mining for Business Intelligence
Autumn Semester 2014, Session 2
Contact Information:
Instructor: Prof. Waleed A. Muhanna ([email protected])
Phone: (614) 292-3808; Fax: 292-2118
Office: 400C Fisher Hall
Office Hours: TR 1:00-2:00, and by appointment.
Course Overview:
Advances in information technologies and the increased digitization of business have led
to an explosive growth in the amount of structured and unstructured data collected and stored in
databases and other electronic repositories. Much—but certainly not all—of this data comes
from operational business software (e.g., finance/accounting applications, Enterprise Resource
Management (ERP), Customer Relationship Management (CRM), workflow and document
management systems, surveillance and monitoring systems, and Web logs) and is often archived
into vast data warehouses to become part of corporate memory. The result of this massive
accumulation of data is that organizations have become data-rich yet still knowledge-poor. What
can be learned from these mountains of data to improve decisions? How can an organization
leverage its massive data warehouses for strategic advantage? A large number of methods with
roots in statistics, informational retrieval and machine learning have been developed to address
the issue of knowledge extraction from data sets—both small and large. The term "data-mining"
refers to this collection of methods. These methods have broad applications; they have been
successfully applied in areas as diverse as market-basket analysis of scanner data, customer
relationship management, churn analysis, direct marketing, fraud detection, click-stream
analysis, personalization and recommendation systems, risk management and credit scoring.
The key objectives of this course are two-fold: (1) to provide you with a theoretical and
practical understanding of core data mining concepts and techniques; and (2) to provide you with
hands-on experience in applying these techniques to practical real-word business problems using
commercial data mining software. As an applied course, the emphasis will be less on the inner
working of each method and more on when and how to use each technique and how to interpret
the results.
Business Intelligence is a process for increasing the competitive advantage of a business
through the intelligent use of available data in decision making. Business intelligence systems
combine operational and other data with data mining tools to improve the timeliness and quality
2
of inputs to decision processes. Broadly defined, data mining is the process of selection,
exploration and modeling of (often, but not necessarily, large) data sets, in order to discover
predictive and descriptive models and patterns. It encompasses both top-down (confirmatory or
hypothesis driven) analysis using traditional statistical techniques and bottom-up (exploratory)
analysis using database and machine learning techniques to discover regularities, relations, or
“local structure/patterns” that are at first unknown. (The confirmative tools of top-down analysis
can then be used to confirm the discoveries and evaluate the quality of decisions based on those
discoveries.) In keeping with this broad definition, topics and related methods discussed include
information retrieval and enterprise reporting, classification, predictive modeling, clustering,
association rules mining, and social network analysis. The application of these methods will be
illustrated using modern software tools via examples, homework assignments and group term
projects.
Upon completion of this course, students should be able to:
1. Fully appreciate the concept of data as a strategic resource;
2. Use existing data retrieval and manipulation tools for data/information extraction and
enterprise reporting;
3. Understand how and when data mining can be used as a problem-solving technique;
4. Describe different methods of data mining;
5. Select an appropriate data mining technique for a specific problem;
6. Use existing data mining software to mine a prepared data set; and
7. Interpret and evaluate the results of data mining.
Prerequisites:
The course is specifically designed with MBA/MACC students as the intended target
audience. The key prerequisites consist of good graduate standing and completion of an
introductory course in probability and statistics. Assignments do not involve programming, per
se, and no prior professional IT experience is assumed.
Course Materials:
? Textbook: Data Mining for Business Intelligence, 2
nd
Edition, by Galit Shmueli, Nitin
R. Patel, and Peter C. Bruce (Wiley: 2010).
? A set of articles, assignments, tutorials, data sets, lecture notes, and various
supplementary materials which will made available through the course homepage at:http://fisher.osu.edu/~muhanna.1/datamining.html
Course Organization:
The course will be run as a mixture of lectures, in-class demonstrations, assignments, and
classroom discussions. Readings will be from the required text together with other
supplementary materials. Some material will be covered only in the readings; other will be
covered only in lecture which may depart from the text in either content or order. To maximize
3
learning, classroom discussion and the amount of time spent on different topics will be adjusted
according to the background and interests of the students.
Assignments
In addition to the reading requirements from the text and the supplementary materials,
there will be 4-5 homework assignments, spaced out over the course of the 7-weeks term. They
are designed to reinforce your understanding of the topics covered. Assignments are to be
handed in on or before the class period of the due date. No late work is accepted. A limited
amount of cooperation among students on homework and lab assignments is permitted. You may
discuss with classmates general solution strategies. However, everyone should independently do
and turn in his/her own work.
Exam
There will be one in-class exam: a final exam. The exam is closed-book and closed-notes,
and it will be held in accordance with Fisher Graduate Programs schedule during the final
examination period on Tuesday, December 16, 2014 at 3:00 p.m. The exam is designed to
assess each student's (a) command of factual knowledge and concepts from the course; and (b)
his or her ability to integrate and generalize these concepts and principles and apply them to new
situations. The format of the exam will primarily be problems and short essay questions. The
final exam must be taken during its scheduled time; make up exams will only be given for
special and compelling cases, in accordance with University guidelines.
Team-Based Term Project
Students will have the opportunity to further sharpen their skills and acquire hands-on
experience with practical databases and real data mining problems through a term project. The
projects will be carried out in teams of 3-4 students and involve the use of DM software.
Although I am generally open to suggestions, each project will normally involve the selection,
design, and performance of a data mining plan using a public data set (such as those provided by
the SAS Institute or in the UCI KDD Archive (http://kdd.ics.uci.edu/) or a non-proprietary data
set available through private student contacts. A handout about the project will be made
available online at the beginning of the course. Teams will submit a written project proposal
partway through the term, followed by a written report and, if time permits, brief class
presentation on the project during the last class meeting.
Software
The methods discussed in this class are computationally intensive and non-trivial; they
cannot be performed using Excel. Fortunately, these methods have matured enough to the point
where they are now implemented in commercial software. We will use Microsoft Access to
familiarize you with relational query language SQL, the industry standard for data extraction,
summarization and enterprise reporting. XLMiner, an EXCEL © add-in, will be introduced in
class and used by students to do assignments and solve business problems using data mining
techniques. If you buy a new copy of the textbook, your copy should include a complementary
4
6-month license to XLMiner (in the back of the book you will find an insert that contains the
license for downloading the add-in). Alternatively, I will provide you with a special Textbook
Code and Course Code that will enable you to download the software, and use it throughout the
term with a 140-day license.
Participation
A portion of the final grade will be based on your class attendance and active participation,
elements that are crucial to the success of class meetings. Attendance refers to punctual
attendance. Your fellow students and I will expect you to come fully prepared to answer
questions and discuss the assigned readings. Each individual is expected to actively and
constructively contribute to class discussions. Good contributions transcend assigned readings
and are inspired, timely, analytical, and relevant to the topics discussed. Students can also earn
participation credit by drawing attention to related development, information and resources
dealing with related topics. Your class participation grade will reflect my judgment of the
quality and quantity of your contributions during the entire term.
Cold calling: On occasion, I will make “cold calls”. This is not intended to put you on the spot
but to encourage class discussion and participation.
Evaluation:
30% of the final grade will be based on graded homework assignments. The final exam
will account for 40% of your grade. The group term project will account for 20% of the grade.
The remaining 10% is assigned to class participation. Final grades will be based on overall class
performance.
Feedback and Continuous Improvement:
Students are strongly encouraged to visit with me in my office and/or use e-mail to ask
questions, to share suggestions about any aspect of the course, or to clear up possible points of
confusion. I will use your feedback to continuously improve and fine-tune the coverage levels
and the teaching/learning processes. Please note that I may not always be able to make all of the
changes suggested, but I will do my best to accommodate your suggestions.
Standards of Integrity and Conduct:
Academic integrity is essential to maintaining an environment that fosters excellence in
teaching, research, and other educational and scholarly activities. Each student in this course is
expected to be familiar with and abide by the principles and standards set forth in The Ohio State
University’s code of student conduct and code of academic conduct. You can view these
documents or download pdf versions at:http://studentaffairs.osu.edu/resource_csc.asphttp://www.gradsch.ohio-state.edu/academic-and-research-misconduct.html
5
It is also expected that each student will behave in a manner that is consistent with the Fisher
Honor Statement, which reads as follows:
As a member of the Fisher College of Business Community, I am personally committed to
the highest standards of behavior. Honesty and integrity are the foundations from which
I will measure my actions. I will hold myself accountable to adhere to these standards.
As a future leader in the community and business environment, I pledge to live by these
principles and celebrate those who share these ideals.
Students with Disabilities:
Any student who feels s/he may need an accommodation based on the impact of a
disability should contact me privately to discuss your specific needs. I rely on the Office for
Disability Services for assistance in verifying the need for accommodations and developing
accommodation strategies. If you have special needs and have not previously contacted the
Office for Disability Services, I encourage you to do so.
Tentative Course Schedule:
The following schedule gives the general plan for the course; changes may be made at
my discretion but are designed to optimize the quality and flow of the content. The course web
site gives the dynamic picture and is an integral part of the class; please make sure to check it on
a regular basis.
Session & Date Topics and Required Readings
Session 1
(T 10/21)
Course Introduction
? Overview/goals of data mining
? Myths about data mining
? The Data Mining process
Readings:
? Data, data everywhere, The Data deluge, The Economist, 2/10.
? Big Data: The Management Revolution, HBR, 10/12.
? Cases:
o “Diamonds in the Data Mine” HBR, 5/03 (PDF)
o “A golden vein” The Economist, 1/04. (PDF)
o “How Verizon cut Customer churn” Financial Express,
10/03. (PDF)
? TB: Chapters 1 & 2
Session 2
(R 10/23)
Data Extraction and Manipulation
? The Relational Data Model and Relational DBMS
? Enterprise Reporting
6
? Relational Algebra
? SQL: The Relational Query Language
Assignments:
? Assignment 1
Session 3
(T 10/28)
OLAP and Multidimensional Data Analysis
? Datawarehousing and Multidimensional Databases
? Data Quality
? Summarization and Data Cubes
? OLAP Tools and Pivot Tables
Readings:
? (Check course web site)
? “An Introduction to OLAP Multidimensional Terminology and
Technology” (PDF)
Session 4
(R 10/30)
Data Exploration and Dimension Reduction
? Data Summarization and Visualization
? Correlation Analysis
? Principal Component Analysis
Readings:
? TB: Chapters 3 & 4
Sessions 5 & 6
(T 11/4 & R 11/6 )
Classification and Predictive Modeling
? Decision Tree induction
? Model Evaluation and Interpretation
Readings:
? TB: Chapters 9 & 5
Assignments:
? Assignment 2
Sessions 7 & 8
(R 11/13 & T 11/18)
Predictive Modeling Using Regression
? Review of OLS Regression
? Logistic Regression
? Model Evaluation and Interpretation
Readings:
? TB: Chapters 6 & 10
Assignments:
? Assignment 3
7
Session 9
(R 11/20)
Predictive Modeling Using Neural Networks & Ensemble Methods
? Introduction to Neural Networks
? Neural Networks vs. Regression
? Model Ensemble
Readings:
? TB: Chapter 11
Sessions 10 & 11
(T 11/25 & T 12/2)
Association & Market-Basked Analysis
? Frequent Itemset and Association Rule Mining
? Pattern evaluation (subjective and objective interestingness measures)
? Sequential patterns
Readings:
? TB: Chapter 13
? R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association
Rules,” Proc. 20th Int. Conf. Very Large Data Bases (VLDB), 1994.
(PDF; only skim)
Assignments:
? Assignment 4
Sessions 12 & 13
(R 12/4 & T 12/9)
Cluster Analysis
? Segmentation and Personalization
? The K-means algorithm
? Hierarchical (Agglomerative) Clustering
? Cluster Validation and Interpretation
Readings:
? TB: Chapter 14
Assignments:
? Assignment 5
Session 14
(R 12/11)
Link and Social Network Analysis
? Network Centrality / Centralization
? Identifying Prestige / Influence
? Discovering Key Opinion Leaders
? Tracking Organized Networks
(T 12/16) Final Exam
8
About The Instructor:
Dr. Waleed A. Muhanna is a Professor and Department Chair of
Accounting & Management Information Systems at the Fisher College of
Business, The Ohio State University. He received his undergraduate
degree in computer science from the University of Tulsa, and holds a
master’s degree in computer science and doctorate in management
information systems from the University of Wisconsin—Madison. Dr.
Muhanna’s teaching and consulting activities span a number of areas, with
particular emphasis on e-commerce, data management and mining, and
information systems strategy. Professor Muhanna’s current research
focuses on IT strategy, data analytics, assessing the business value of
information technology, and understanding the impact of information
technology, including the Internet, on organizations and markets. His other research interests
include trust and reputation online, e-commerce strategy, model and database management
systems, and system performance modeling and evaluation. Professor Muhanna has published
numerous articles in scholarly journals, including Management Science, MIS Quarterly,
Strategic Management Journal, Decision Sciences, the Journal of Information Systems, the
International Journal of Accounting Information Systems, ACM Transactions on Computer
Systems, IEEE Transactions on Software Engineering, Communications of the ACM, Decision
Support Systems, Information & Management, European Journal of Operational Research,
Computers in Human Behavior, and the Annals of Operations Research. He recently served as
the Director of the Ph.D. Program in Accounting & MIS, and currently serves as the Academic
Director of the Center for Business Performance Management. Professor Muhanna is the past
Vice-Chair of INFORMS' Information Systems Society, has recently completed his service as an
Associate Editor at Information Systems Research, and currently serves on the editorial boards of
leading academic journals, including Management Science, Information Technology and
Management, and the International Journal of Accounting Information Systems.
doc_455441113.pdf
Department of Accounting and Management Information Systems
1
The Ohio State University
The Max M. Fisher College of Business
Department of Accounting and Management Information Systems
AMIS 7640 – Data Mining for Business Intelligence
Autumn Semester 2014, Session 2
Contact Information:
Instructor: Prof. Waleed A. Muhanna ([email protected])
Phone: (614) 292-3808; Fax: 292-2118
Office: 400C Fisher Hall
Office Hours: TR 1:00-2:00, and by appointment.
Course Overview:
Advances in information technologies and the increased digitization of business have led
to an explosive growth in the amount of structured and unstructured data collected and stored in
databases and other electronic repositories. Much—but certainly not all—of this data comes
from operational business software (e.g., finance/accounting applications, Enterprise Resource
Management (ERP), Customer Relationship Management (CRM), workflow and document
management systems, surveillance and monitoring systems, and Web logs) and is often archived
into vast data warehouses to become part of corporate memory. The result of this massive
accumulation of data is that organizations have become data-rich yet still knowledge-poor. What
can be learned from these mountains of data to improve decisions? How can an organization
leverage its massive data warehouses for strategic advantage? A large number of methods with
roots in statistics, informational retrieval and machine learning have been developed to address
the issue of knowledge extraction from data sets—both small and large. The term "data-mining"
refers to this collection of methods. These methods have broad applications; they have been
successfully applied in areas as diverse as market-basket analysis of scanner data, customer
relationship management, churn analysis, direct marketing, fraud detection, click-stream
analysis, personalization and recommendation systems, risk management and credit scoring.
The key objectives of this course are two-fold: (1) to provide you with a theoretical and
practical understanding of core data mining concepts and techniques; and (2) to provide you with
hands-on experience in applying these techniques to practical real-word business problems using
commercial data mining software. As an applied course, the emphasis will be less on the inner
working of each method and more on when and how to use each technique and how to interpret
the results.
Business Intelligence is a process for increasing the competitive advantage of a business
through the intelligent use of available data in decision making. Business intelligence systems
combine operational and other data with data mining tools to improve the timeliness and quality
2
of inputs to decision processes. Broadly defined, data mining is the process of selection,
exploration and modeling of (often, but not necessarily, large) data sets, in order to discover
predictive and descriptive models and patterns. It encompasses both top-down (confirmatory or
hypothesis driven) analysis using traditional statistical techniques and bottom-up (exploratory)
analysis using database and machine learning techniques to discover regularities, relations, or
“local structure/patterns” that are at first unknown. (The confirmative tools of top-down analysis
can then be used to confirm the discoveries and evaluate the quality of decisions based on those
discoveries.) In keeping with this broad definition, topics and related methods discussed include
information retrieval and enterprise reporting, classification, predictive modeling, clustering,
association rules mining, and social network analysis. The application of these methods will be
illustrated using modern software tools via examples, homework assignments and group term
projects.
Upon completion of this course, students should be able to:
1. Fully appreciate the concept of data as a strategic resource;
2. Use existing data retrieval and manipulation tools for data/information extraction and
enterprise reporting;
3. Understand how and when data mining can be used as a problem-solving technique;
4. Describe different methods of data mining;
5. Select an appropriate data mining technique for a specific problem;
6. Use existing data mining software to mine a prepared data set; and
7. Interpret and evaluate the results of data mining.
Prerequisites:
The course is specifically designed with MBA/MACC students as the intended target
audience. The key prerequisites consist of good graduate standing and completion of an
introductory course in probability and statistics. Assignments do not involve programming, per
se, and no prior professional IT experience is assumed.
Course Materials:
? Textbook: Data Mining for Business Intelligence, 2
nd
Edition, by Galit Shmueli, Nitin
R. Patel, and Peter C. Bruce (Wiley: 2010).
? A set of articles, assignments, tutorials, data sets, lecture notes, and various
supplementary materials which will made available through the course homepage at:http://fisher.osu.edu/~muhanna.1/datamining.html
Course Organization:
The course will be run as a mixture of lectures, in-class demonstrations, assignments, and
classroom discussions. Readings will be from the required text together with other
supplementary materials. Some material will be covered only in the readings; other will be
covered only in lecture which may depart from the text in either content or order. To maximize
3
learning, classroom discussion and the amount of time spent on different topics will be adjusted
according to the background and interests of the students.
Assignments
In addition to the reading requirements from the text and the supplementary materials,
there will be 4-5 homework assignments, spaced out over the course of the 7-weeks term. They
are designed to reinforce your understanding of the topics covered. Assignments are to be
handed in on or before the class period of the due date. No late work is accepted. A limited
amount of cooperation among students on homework and lab assignments is permitted. You may
discuss with classmates general solution strategies. However, everyone should independently do
and turn in his/her own work.
Exam
There will be one in-class exam: a final exam. The exam is closed-book and closed-notes,
and it will be held in accordance with Fisher Graduate Programs schedule during the final
examination period on Tuesday, December 16, 2014 at 3:00 p.m. The exam is designed to
assess each student's (a) command of factual knowledge and concepts from the course; and (b)
his or her ability to integrate and generalize these concepts and principles and apply them to new
situations. The format of the exam will primarily be problems and short essay questions. The
final exam must be taken during its scheduled time; make up exams will only be given for
special and compelling cases, in accordance with University guidelines.
Team-Based Term Project
Students will have the opportunity to further sharpen their skills and acquire hands-on
experience with practical databases and real data mining problems through a term project. The
projects will be carried out in teams of 3-4 students and involve the use of DM software.
Although I am generally open to suggestions, each project will normally involve the selection,
design, and performance of a data mining plan using a public data set (such as those provided by
the SAS Institute or in the UCI KDD Archive (http://kdd.ics.uci.edu/) or a non-proprietary data
set available through private student contacts. A handout about the project will be made
available online at the beginning of the course. Teams will submit a written project proposal
partway through the term, followed by a written report and, if time permits, brief class
presentation on the project during the last class meeting.
Software
The methods discussed in this class are computationally intensive and non-trivial; they
cannot be performed using Excel. Fortunately, these methods have matured enough to the point
where they are now implemented in commercial software. We will use Microsoft Access to
familiarize you with relational query language SQL, the industry standard for data extraction,
summarization and enterprise reporting. XLMiner, an EXCEL © add-in, will be introduced in
class and used by students to do assignments and solve business problems using data mining
techniques. If you buy a new copy of the textbook, your copy should include a complementary
4
6-month license to XLMiner (in the back of the book you will find an insert that contains the
license for downloading the add-in). Alternatively, I will provide you with a special Textbook
Code and Course Code that will enable you to download the software, and use it throughout the
term with a 140-day license.
Participation
A portion of the final grade will be based on your class attendance and active participation,
elements that are crucial to the success of class meetings. Attendance refers to punctual
attendance. Your fellow students and I will expect you to come fully prepared to answer
questions and discuss the assigned readings. Each individual is expected to actively and
constructively contribute to class discussions. Good contributions transcend assigned readings
and are inspired, timely, analytical, and relevant to the topics discussed. Students can also earn
participation credit by drawing attention to related development, information and resources
dealing with related topics. Your class participation grade will reflect my judgment of the
quality and quantity of your contributions during the entire term.
Cold calling: On occasion, I will make “cold calls”. This is not intended to put you on the spot
but to encourage class discussion and participation.
Evaluation:
30% of the final grade will be based on graded homework assignments. The final exam
will account for 40% of your grade. The group term project will account for 20% of the grade.
The remaining 10% is assigned to class participation. Final grades will be based on overall class
performance.
Feedback and Continuous Improvement:
Students are strongly encouraged to visit with me in my office and/or use e-mail to ask
questions, to share suggestions about any aspect of the course, or to clear up possible points of
confusion. I will use your feedback to continuously improve and fine-tune the coverage levels
and the teaching/learning processes. Please note that I may not always be able to make all of the
changes suggested, but I will do my best to accommodate your suggestions.
Standards of Integrity and Conduct:
Academic integrity is essential to maintaining an environment that fosters excellence in
teaching, research, and other educational and scholarly activities. Each student in this course is
expected to be familiar with and abide by the principles and standards set forth in The Ohio State
University’s code of student conduct and code of academic conduct. You can view these
documents or download pdf versions at:http://studentaffairs.osu.edu/resource_csc.asphttp://www.gradsch.ohio-state.edu/academic-and-research-misconduct.html
5
It is also expected that each student will behave in a manner that is consistent with the Fisher
Honor Statement, which reads as follows:
As a member of the Fisher College of Business Community, I am personally committed to
the highest standards of behavior. Honesty and integrity are the foundations from which
I will measure my actions. I will hold myself accountable to adhere to these standards.
As a future leader in the community and business environment, I pledge to live by these
principles and celebrate those who share these ideals.
Students with Disabilities:
Any student who feels s/he may need an accommodation based on the impact of a
disability should contact me privately to discuss your specific needs. I rely on the Office for
Disability Services for assistance in verifying the need for accommodations and developing
accommodation strategies. If you have special needs and have not previously contacted the
Office for Disability Services, I encourage you to do so.
Tentative Course Schedule:
The following schedule gives the general plan for the course; changes may be made at
my discretion but are designed to optimize the quality and flow of the content. The course web
site gives the dynamic picture and is an integral part of the class; please make sure to check it on
a regular basis.
Session & Date Topics and Required Readings
Session 1
(T 10/21)
Course Introduction
? Overview/goals of data mining
? Myths about data mining
? The Data Mining process
Readings:
? Data, data everywhere, The Data deluge, The Economist, 2/10.
? Big Data: The Management Revolution, HBR, 10/12.
? Cases:
o “Diamonds in the Data Mine” HBR, 5/03 (PDF)
o “A golden vein” The Economist, 1/04. (PDF)
o “How Verizon cut Customer churn” Financial Express,
10/03. (PDF)
? TB: Chapters 1 & 2
Session 2
(R 10/23)
Data Extraction and Manipulation
? The Relational Data Model and Relational DBMS
? Enterprise Reporting
6
? Relational Algebra
? SQL: The Relational Query Language
Assignments:
? Assignment 1
Session 3
(T 10/28)
OLAP and Multidimensional Data Analysis
? Datawarehousing and Multidimensional Databases
? Data Quality
? Summarization and Data Cubes
? OLAP Tools and Pivot Tables
Readings:
? (Check course web site)
? “An Introduction to OLAP Multidimensional Terminology and
Technology” (PDF)
Session 4
(R 10/30)
Data Exploration and Dimension Reduction
? Data Summarization and Visualization
? Correlation Analysis
? Principal Component Analysis
Readings:
? TB: Chapters 3 & 4
Sessions 5 & 6
(T 11/4 & R 11/6 )
Classification and Predictive Modeling
? Decision Tree induction
? Model Evaluation and Interpretation
Readings:
? TB: Chapters 9 & 5
Assignments:
? Assignment 2
Sessions 7 & 8
(R 11/13 & T 11/18)
Predictive Modeling Using Regression
? Review of OLS Regression
? Logistic Regression
? Model Evaluation and Interpretation
Readings:
? TB: Chapters 6 & 10
Assignments:
? Assignment 3
7
Session 9
(R 11/20)
Predictive Modeling Using Neural Networks & Ensemble Methods
? Introduction to Neural Networks
? Neural Networks vs. Regression
? Model Ensemble
Readings:
? TB: Chapter 11
Sessions 10 & 11
(T 11/25 & T 12/2)
Association & Market-Basked Analysis
? Frequent Itemset and Association Rule Mining
? Pattern evaluation (subjective and objective interestingness measures)
? Sequential patterns
Readings:
? TB: Chapter 13
? R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association
Rules,” Proc. 20th Int. Conf. Very Large Data Bases (VLDB), 1994.
(PDF; only skim)
Assignments:
? Assignment 4
Sessions 12 & 13
(R 12/4 & T 12/9)
Cluster Analysis
? Segmentation and Personalization
? The K-means algorithm
? Hierarchical (Agglomerative) Clustering
? Cluster Validation and Interpretation
Readings:
? TB: Chapter 14
Assignments:
? Assignment 5
Session 14
(R 12/11)
Link and Social Network Analysis
? Network Centrality / Centralization
? Identifying Prestige / Influence
? Discovering Key Opinion Leaders
? Tracking Organized Networks
(T 12/16) Final Exam
8
About The Instructor:
Dr. Waleed A. Muhanna is a Professor and Department Chair of
Accounting & Management Information Systems at the Fisher College of
Business, The Ohio State University. He received his undergraduate
degree in computer science from the University of Tulsa, and holds a
master’s degree in computer science and doctorate in management
information systems from the University of Wisconsin—Madison. Dr.
Muhanna’s teaching and consulting activities span a number of areas, with
particular emphasis on e-commerce, data management and mining, and
information systems strategy. Professor Muhanna’s current research
focuses on IT strategy, data analytics, assessing the business value of
information technology, and understanding the impact of information
technology, including the Internet, on organizations and markets. His other research interests
include trust and reputation online, e-commerce strategy, model and database management
systems, and system performance modeling and evaluation. Professor Muhanna has published
numerous articles in scholarly journals, including Management Science, MIS Quarterly,
Strategic Management Journal, Decision Sciences, the Journal of Information Systems, the
International Journal of Accounting Information Systems, ACM Transactions on Computer
Systems, IEEE Transactions on Software Engineering, Communications of the ACM, Decision
Support Systems, Information & Management, European Journal of Operational Research,
Computers in Human Behavior, and the Annals of Operations Research. He recently served as
the Director of the Ph.D. Program in Accounting & MIS, and currently serves as the Academic
Director of the Center for Business Performance Management. Professor Muhanna is the past
Vice-Chair of INFORMS' Information Systems Society, has recently completed his service as an
Associate Editor at Information Systems Research, and currently serves on the editorial boards of
leading academic journals, including Management Science, Information Technology and
Management, and the International Journal of Accounting Information Systems.
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