1 Nova Southeastern University College of Computing and Engineering Master of Management Information Systems MMIS 643 Data Mining Fall 2019 (August 19 – December 8, 2019) Class Project Due Date: November 17, 2019 (Firm) Instructor: Dr. Junping Sun In this project, you will be expected to do a comprehensive literature search and survey, select and study a specific topic in one subject area of data mining and its applications in business intelligence and analytics (BIA), and write a research paper on the selected topic by yourself. The research paper you are required to write can be a detailed comprehensive study on some specific topic or the original research work that will have been done by yourself. Requirements and Instructions for the Research Paper: 1. The objective of the paper should be very clear about subject, scope, domain, and the goals to be achieved. 2. The paper should address the important advanced and critical issues in a specific area of data mining and its applications in business intelligence and analytics. Your research paper should emphasize not only breadth of coverage, but also depth of coverage in the specific area. 3. The research paper should give the measurable conclusions and future research directions (this is your contribution). 4. It might be beneficial to review or browse through about 15 to 20 relevant technical articles before you make decision on the topic of the research project. 5. The research paper can be: a. Literature review papers on data mining techniques and their applications for business intelligence and analytics. b. Study and examination of data mining techniques in depth with technical details. c. Applied research that applies a data mining method to solve a real world application in terms of the domain of BIA. 6. The research paper should reflect the quality at certain academic research level. 7. The paper should be about at least 3000-3500 words double space. 8. The paper should include adequate abstraction or introduction, and reference list. 9. Please write the paper in your words and statements, and please give the names of references, citations, and resources of reference materials if you want to use the statements from other reference articles. 2 10. From the systematic study point of view, you may want to read a list of technical papers from relevant magazines, journals, conference proceedings and theses in the area of the topic you choose. 11. For the format and style of your research paper, please make reference to CEC Dissertation Guide (http://cec.nova.edu/doctoral/documents/nsu-cec-dissertation-guides.html), Publication Manual of APA, or the format of ACM and IEEE journal publications. Suggested and Possible Topics for Written Report (But Not Limited) Supervised Learning Methods: Classification Methods: Regression Methods Multiple Linear Regression Logistic Regression ...

1. 1
Nova Southeastern University
College of Computing and Engineering
Master of Management Information Systems
MMIS 643 Data Mining
Fall 2019
(August 19 – December 8, 2019)
Class Project
Due Date: November 17, 2019 (Firm)
Instructor: Dr. Junping Sun
In this project, you will be expected to do a comprehensive
literature search and survey, select and
study a specific topic in one subject area of data mining and its
applications in business
intelligence and analytics (BIA), and write a research paper on
the selected topic by yourself. The
research paper you are required to write can be a detailed
comprehensive study on some specific
topic or the original research work that will have been done by
yourself.
Requirements and Instructions for the Research Paper:

2. 1. The objective of the paper should be very clear about subject,
scope, domain, and the goals to be
achieved.
2. The paper should address the important advanced and critical
issues in a specific area of data
mining and its applications in business intelligence and
analytics. Your research paper
should emphasize not only breadth of coverage, but also depth
of coverage in the specific area.
3. The research paper should give the measurable conclusions
and future research directions (this is
your contribution).
4. It might be beneficial to review or browse through about 15
to 20 relevant technical articles
before you make decision on the topic of the research project.
5. The research paper can be:
a. Literature review papers on data mining techniques and their
applications for business
intelligence and analytics.
b. Study and examination of data mining techniques in depth
with technical details.
c. Applied research that applies a data mining method to solve a
real world application in terms
of the domain of BIA.

3. 6. The research paper should reflect the quality at certain
academic research level.
7. The paper should be about at least 3000-3500 words double
space.
8. The paper should include adequate abstraction or
introduction, and reference list.
9. Please write the paper in your words and statements, and
please give the names of
references, citations, and resources of reference materials if you
want to use the statements from
other reference articles.
2
10. From the systematic study point of view, you may want to
read a list of technical papers from
relevant magazines, journals, conference proceedings and theses
in the area of the topic you
choose.
11. For the format and style of your research paper, please make
reference to CEC Dissertation
Guide (http://cec.nova.edu/doctoral/documents/nsu-cec-
dissertation-guides.html), Publication

4. Manual of APA, or the format of ACM and IEEE journal
publications.
Suggested and Possible Topics for Written Report (But Not
Limited)
Supervised Learning Methods:
Classification Methods:
Regression Methods
Multiple Linear Regression
Logistic Regression
Ordered Logistic and Ordered Probit Regression Models
Multinomial Logistic Regression Model
Poisson and Negative Binomial Regression Models
Bayesian Classification
Naïve Bayes Method
k Nearest Neighbors
Decision Trees
ID3 (Iterative Dichotomiser 3)
C4.5 and C5.0
CART (Classification and Regression Trees)
Scalable Decision Tree Techniques
Neural Network-Based Methods
Back Propagation
Neural Network Supervised Learning
Bayes Belief Network
Rule-Based Methods
Generating Rules from a Decision Tree

5. Generating Rules from a Neural Net
Generating Rules without Decision Tree or Neural Net
Support Vector Machine
AdaBoost (Adaptive Boosting)
XGBoost
GBM
Ensemble Methods
Bagging and Boosting
Random Forest
3
RainForest
Fuzzy Set and Rough Set Methods
Unsupervised Learning Methods:
Clustering Methods:
Partition Based Methods
Squared Error Clustering
K-Means Clustering (Centroid-Based Technique)
K-Medoids Method (Partition Around Medoids, Representative
Object-Based Technique)
Bond Energy
Hierarchical Methods
Agnes(Agglomerative vs. Divisive Hierarchical Clustering)

6. BIRCH (Balanced Iterative Reducing and Clustering Using
Hierarchies)
Chameleon (Hierarchical Clustering using Dynamic Modeling)
CLARANS (Clustering Large Applications Based Upon
Randomized Search)
CURE (Clustering Using REpresentatives)
Density Based Methods
DBSCAN (Density Based Spatial Clustering of Applications
with Noise, Density Based
Clustering Based on Connected Regions with High Density)
OPTICS (Ordering Points to Identity the Clustering Structure)
DENCLUE (DENsity Based CLUstEring, Clustering Based on
Density Distribution Functions)
Grid-Based Methods
STING (Statistical Information Grid)
CLIQUE (Clustering In QUEst, An Apriori-like Subspace
Clustering Method)
Probabilistic Model Based Clustering
Clustering Graph and Network Data (For Example, Social
Networks)
Self-Organized Map Technique
Evaluation and Performance Measurement of Clustering
Methods
Assessing Clustering Technology
Determining the Number of Clusters
Measuring Clustering Quality
Association Rule Mining

7. Evolution Based Methods:
Genetic Algorithms
Applications:
4
Data Mining Applications for Business Intelligence and
Analytics
Text Mining
Spatial Mining
Temporal Mining
Web Mining
Others:
Over fitting and Under fitting issues
Outliers
Performance Evaluation and Measurement
Confusion Matrix
ROC (Receiver Operating Characteristic)
AUC (Area Under the Curve)
Data Mining Tools
XLMiner
RapdiMiner
Weka
NodeXL
TensorFlow

8. 5

9. Sample Format of Project Report
1. Title Page
In general, the number of words in the title of report should be
limited around 10 words if
possible. The title page must include, the term date, your name,
email, contact
information, etc. below the paper title.
2. Abstract
The abstract page should summarize the highlight of your
project to tell the audience what have
been done in the research project.
3. Table of Contents
The TOC part should list all the titles of sections and
subsections with page numbers.
4. Introduction
This part introduces the audience with necessary information to
guide them into the subjects of
your research project.
5. Background and Literature Review

10. 6. Statement of the Proposed Research or Study
With the discussion in Background and Literature Review, the
proposed research and study can
be given in the format of, possibly, Problem Statement or
Objective of Study to indicate what to
be studied, investigated, researched, and/or achieved from this
project.
7. Methodology
Based on the Problem Statement and the objective to be
achieved, you may want to elaborate the
underline methodology to be used in order to fulfill the research
task and achieve the goal of the
research/study. If possible, please provide elaboration of
rationales in both depth and width.
It is better to use illustrative examples to explain the
methodology employed in this project.
8. Experiment Design and Result Analysis
Provide the details of how experiments are designed and
conducted, and observation from the
experiment. Analysis of experimental results are important
based on your observation,
understanding, interpretation, etc. with some performance
analysis methods.
9. Conclusion

11. Summarize your research/study by giving some conclusion
from the project, and may provide
future research/study directions with discussion of potentials.
10. Reference List
6
11. Appendix (if necessary)
For style, please make reference to APA Manual, ACM, IEEE
publications, CEC
Dissertation Guide.
7
Certification of Authorship

12. Submitted to (Advisor’s Name):
Student’s Name:
Date of Submission:
Purpose and Title of Submission:
Certification of Authorship: I hereby certify that I am the author
of this document and
that any assistance I received in its preparation is fully
acknowledged and disclosed in the
document. I have also cited all sources from which I obtained
data, ideas, or words that
are copied directly or paraphrased in the document. Sources are
properly credited
according to accepted standards for professional publications. I
also certify that this paper
was prepared by me for this purpose.
Student's
Signature:____________________________________________
____________
1
Differential Instruction

13. Differential Instruction
EDU 381 Curriculum and Instructional Design
2
Differential Instruction
Differential Instruction
“Differentiated Instruction is the way in which a teacher
anticipates and responds to
a variety of student needs in the classroom.” (Carlson, n.d.) It is
a 4-level process that

14. enables the teacher to adjust their lesson plan to successfully
engage each individual
student and help them to understand the concept that is being
taught.
Theoretical or Research Background
The theoretical basis for Differential Instruction (DI) is that
each student learns
differently, therefore the teacher must be able to teach a mixed
group of learners with
ease. DI provides ways for a teacher to assist the students that
needs little extra help than
their peers. A study was done in 2010 in Cyprus (an island in
the Mediterranean Sea off
the southern coast of Turkey) on DI and its effectiveness in the
classroom. Two test
groups comprised of 4th grade students spread out over 24
classrooms; 14 classrooms
were taught using the DI method, 10 classrooms were the
control group and taught
traditionally. The study results show that “differentiation is
feasible, effective and
necessary in order to promote quality and equity dimension of
effectiveness” in a mixed-

15. ability classroom. (Valiande, et al., 2010, page 15)
DI is a 4-level model that allows the teacher to adjust their
lesson plans to be able to
include each of their students in a mixed-ability class. Level 1
is for the general
education, “What does the teacher want the student to learn?”
There teacher wants to
ensure that there will be a solid foundation for their lesson plan
to be built upon. Their
plan “should include attention to respectful tasks, quality
curriculum, teaching up,
flexible grouping, continual assessment and building
community.” (Hansen, et al., 2015,
3
Differential Instruction
Section 2.3) The second level is the original plan itself. It is
composed of the content that
is to be learned, the process that the teacher will use to teach
the content, the product that
will be the end result and how engaged the teacher will predict
the students to be. When
all of these factors are combined the teacher has worked
towards a positive learning

16. environment for his/her classroom. Level 3 is where the
adjustments to the original lesson
plan are made. Student characteristics the teacher should take
into consideration include
readiness, interest and the individual learning profiles of each
student in the class. The
fourth and final level consists of several DI strategies that can
be used. For example,
“Acting Out a Problem: students can act out mathematical,
scientific, or social problems
to improve their comprehension.” (Rowan, 2013)
How Does it Work
My “classroom” would consist of mostly preschool age children
working on reading
and their letters. I believe that my students would enjoy the
“Acting Out” scenario from
the above paragraph. I would assign each child a role from a
story based on how much
the child likes to be the center of attention. I will use
“Goldilocks and the 3 Bears” as an
example. If I have a student that loves to talk and who knows
that story I will assign that
student Goldilocks. For a student that is shy and rather quiet, I

17. would give the role of
Baby Bear. The student in the role of Baby Bear would not have
to speak very loud and
could hide behind Mama or Papa Bear, making the shy child
feel involved but not the
center of attention. We would work on sounding out sight
words, counting, right and
wrong behavior and acting out the book.
4
Differential Instruction
Experiences With This Method
An experience that I had with this method occurred over this
past winter, my
nephew, Mason, was home-schooled for Kindergarten and I
would have to play “teacher”
whenever he was spending time with me. Mason had problems
with comparison and
subtraction in math. His mom tried to explain it to him several
times, finally gave up and
asked me to help. Mason was not getting the concept with the
pictures on the screen, and

18. my laptop screen was becoming covered in fingerprints. I
decided to try something a little
more hands-on. Mason loves marshmallows, so I bought a bag
of strawberry star
marshmallows and a bag of white square marshmallows. We
compared the stars to the
squares and subtracted. By the end of the lesson he was able to
compare and subtract on
his own. It took a couple more worksheets before we went back
to the computer screen
comparisons, he still had a small problem without the hands-on
objects but his teacher
said that is something he will grow out of as he becomes more
confident and we can
work on it over the summer.
Questions About the Method
I have several questions but the top one is: how much time do
the teachers give
themselves to learn each student’s abilities and do they plan
general lessons in the
beginning of the school year to get a basic overview of each
child? Another question: is
DI compatible with backward design, and if so where is does the

19. adjustment in the lesson
plan take place? Do the adjustments take place “on the fly” or
are they planned in? There
is still a lot to learn on this particular method.
Conclusions
5
Differential Instruction
I am on the fence concerning this method. I am the type of
person that likes to have a
plan in place. I am more than willing to adjust my plan as
needed. However, this method
seems to be a “adjust as you go” type of method. I believe that
adjusting as the lesson
occurs will lead to a lot of confusion, not just for the student
but for the teacher as well. If
the lesson plans are made at even a slightly advanced date (say
a week in advance), this
does not leave a lot of time for adjustments if a new student
enters the class or a
substitute needs to take over. I am all for a mixed-abilities
classroom, however, I believe
that I prefer the Response to Intervention.

20. 6
Differential Instruction
References
Carlson, Amy Marin. (n.d). What is differentiated instruction?
Examples, Definition &
Activites. Retrieved from
http://study.com/academy/lesson/what-is-differentiated-
instruction-examples-definition-activities.html.
Hansen, C.B., Buczynski, S., & Puckett, K.S. (2015).
Curriculum and Instruction for the
21st Century. Bridgepoint Education.
Rowan, Kelly Jo. (July 27, 2013). Glossary of Instructional
Strategies. Retrieved from

21. http://www.beesburg.com/edtools/glossary.html.
Valiande, A. Stavroula, Kyriakides, Leonidas, and Koutselini,
Mary. (January 2011).
Investigating the Impact of Differentiated Instruction in Mixed
Ability Classrooms:
It’s Impact on the Quality and Equity Dimensions of Education
Effectiveness.
Retrieved from
http://www.icsei.net/icsei2011/Full%20Papers/0155.pdf.