CSCI-B 565 Data Mining
3 credits
- Prerequisite(s): None
- Delivery: On-Campus, Online
- Semesters offered: Fall (Check the schedule to confirm.)
- Equivalent(s): CSCI 57300
Description
This course covers algorithmic and practical aspects of discovering patterns and relationships in large databases. The course also provides hands-on experience in data analysis, clustering, and prediction. Topics include data preprocessing and exploration, data warehousing, association rule mining, classification and regression, clustering, anomaly detection, human factors, and social issues in data mining.
Topics
Introduction and basics
- Introduction to data mining
- Data representation and preprocessing
- Data visualization
Data storage and retrieval
- Data warehouse and data cube
- Data mining on cloud data warehouses
Data analysis techniques
- Univariate and multivariate analysis
- Classification and prediction
- Clustering analysis
- Ensemble learning
- Feature selection
- Model selection and evaluation
Data mining techniques
- Finding similar items
- Mining association rules
- Graph mining
- Link analysis
- Recommendation systems: Content-based, collaborative filtering
Advanced topics
- Big data mining
- Data mining applications and trends
- Case studies on various types of data
- Advertising on the Web
Human factors and ethics
- Social and ethical issues in data mining
- Privacy-preserving data mining
- Intellectual ownership
- Privacy models
- Risk analysis
- User interfaces
- Interestingness and relevance
Learning Outcomes
- Analyze a real-world dataset by determining appropriate data mining techniques and packages. CS 4
- Write a program to implement a data mining algorithm. CS 1
- Design and conduct data mining experiments and report and discuss the results. CS 7
- Evaluate a data mining case study with respect to security, social, and other issues. CS 5
- Critique data mining research in peer-reviewed articles and present it. CS 7
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.