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 between 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.
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.
12. For the title page, please include course number, course name, term/date, your name, contact
information such as email and phone number.
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
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
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)
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
Evolution Based Methods:
Genetic Algorithms
Applications:
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
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, course number, course name, 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
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
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
For style, please make reference to APA Manual, ACM, IEEE publications, CEC Dissertation
Guide.
a) Machine Learning for Business Analytics: Concepts, Techniques, and Applications
with Analytic 4th Edition, 2023
By Galit Shmueli, Peter C. Bruce, and Kuber R. Deokar or use Same book 3rd edition if
4
th edition can’t be found
b) Getting Started with Business Analytics: Insightful Decision-Making (2013)
By David Roi Hardoon and Galit Shmueli
c) Data Science For Business: What You Need To Know About Data Mining And DataAnalytic Thinking(2013)By Foster Provost and Tom Fawcett ,O’Reilly Media
d) Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition
(2011)
By Ian H. Witten, Eibe Frank, and Mark A. Hall
e) An Introduction to Statistical Learning: with Applications in R (2013)
By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshiran