Webster University • Webster Groves, MO • George Herbert Walker School of Business & Technology • Mathematics/Computer Science
Data Mining CSIS-4330
Many businesses are discovering that stored within their corporate data repositories are the elements to understanding and controlling the components of their business processes. Data mining is the process of finding useful patterns in data. The objective of data mining is to use discovered patterns to help explain current behavior or to predict future outcomes. This course will provide a foundation to understand data mining methods and data visualization techniques by examining how these technologies provide useful knowledge that supports critical business decisions. This course will introduce data mining techniques and have the student experiment with data mining applications. Additionally, the course will explain how data visualization can allow managers to quickly access large data repositories and make clear decisions based on integrated information structures.
After successfully completing Data Mining students should be able to:
- Describe the sequence of events associated with a typical data mining project.
- Explain the various techniques use for data preprocessing and cleansing.
- Describe various data classification methods in common use.
- Describe various data clustering methods in common use.
- Describe various methods to create association rules in common use.
- Assess current data mining technologies, including statistical analysis, neural networks, genetic algorithms, fuzzy logic, and decision trees.
- Recommend which intelligent system is best suited for different kinds of problems.
- Summarize the trends in data mining, and visualization.
- Identify the issues of privacy that must be addressed if data mining (DM) is to become a pervasive and effective source of knowledge.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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- Introduction to Data Mining
- Ch 1. Introduction
- Ch 2. Overview of the Data Mining Process
- Ch 3. Data Visualization
- Dimension Reduction and Performance Evaluation
- Ch 4. Dimension Reduction
- Ch 5. Evaluating Predictive Performance
- Linear Regression and k-Nearest-Neighbors
- Ch 6. Multiple Linear Regression
- Ch 7. k-Nearest-Neighbors (k-NN)
Naïve Bayes and Classification Tree
- Ch 8. The Naïve Bayes Classifier
- Ch 9. Classification and Regression Trees
- Midterm Exam
Logistic Regression and Discriminant analysis
- Ch 10. Logistic Regression
- Ch 12. Discriminant Analysis
Mining Relationships among Records
- Ch 14. Association Rules and Collaborative Filtering
- Ch 15. Cluster Analysis
Forecasting Time Series
- Ch 16. Handling Time Series
- Ch 17. Regression-Based Forecasting
Social Network Analytics and Text Mining
- Ch 19. Social Network Analytics
- Ch 20. Text Mining
- Final Exam