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.

2 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.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.
Supervised Learning Methods:
Classification Methods:
Regression Methods
  Multiple Linear Regression  
Logistic Regression
  Ordered Logistic and Ordered Probit Regression Models

3 Multinomial Logistic Regression Model
  Poisson and Negative Binomial Regression Models
Bayesian Classification
Nave 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
11.Appendix (if necessary)
For style, please make reference to APA Manual, ACM, IEEE publications, CEC
Dissertation Guide.