ITCS 8162: Data Mining

University of North Carolina, Charlotte

Exploration of knowledge discovery and data mining algorithms for data analysis, with an emphasis on techniques suitable for large datasets. Topics include: Data Operations (fusion, reduction, sanitization, balancing), Association/Representative Rules, Clustering, High Dimensional & Distributed Data Mining, Dimensionality Reduction, Link Analysis, and Actionability (Action and Meta-Action Rules). The curriculum also spans a diverse range of data mining areas such as Mining Data Streams, Web Mining, Web Advertising, Knowledge-Based Recommendation Systems, and Personalization. Throughout the course, a special focus will be placed on practical applications, showcasing diverse examples from fields like healthcare and business.

Average GPA: 3.94

Grade distribution records: 54 students across 21 terms.

Grade distribution

Based on 54 student grade records across 21 terms and 6 professors.

Instructors

• Zbigniew Ras 22 students, Average GPA 3.95
• Angelina Tzacheva 15 students, Average GPA 3.92
• Yong Ge 9 students, Average GPA 4.00
• Siddharth Krishnan 4 students, Average GPA 3.75
• Laurel Powell 1 students, Average GPA 4.00
• Pamela Thompson 1 students, Average GPA 4.00