Research paper – Data mining and its applications in business intelligence and analytics

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.

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. 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

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