Data Mining and Knowledge Discovery Fundamentals
Master essential data mining techniques including clustering, classification, and pattern mining to extract valuable insights from large datasets.
Master essential data mining techniques including clustering, classification, and pattern mining to extract valuable insights from large datasets.
This intermediate-level course explores the fundamentals of data mining and knowledge discovery, integrating techniques from database management, statistics, and artificial intelligence. Students learn to analyze large data repositories, including databases and web data, using various mining techniques. The course covers core concepts in clustering, classification, frequent pattern mining, and data warehousing, with practical applications for real-world data analysis.
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What you'll learn
Apply clustering techniques to discover natural groupings in data
Implement classification methods for predictive modeling
Discover frequent patterns and association rules in large datasets
Utilize data warehouse techniques for efficient data analysis
Process and analyze streaming data effectively
Work with web databases and large-scale data repositories
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This comprehensive course covers the fundamental concepts and techniques in data mining and knowledge discovery. Students learn various approaches to extract valuable insights from large datasets, including clustering methods for grouping similar data, classification techniques for prediction and analysis, and pattern mining for identifying frequent itemsets. The curriculum also explores data warehousing concepts and techniques for handling streaming data. Special emphasis is placed on practical applications and real-world case studies to demonstrate the effectiveness of different data mining approaches.
Association
Module 1
Clustering
Module 2
Classification
Module 3
Data Warehouse
Module 4
Data Mining over Data Streams
Module 5
Web Database
Module 6
Fee Structure
Instructor
7 Courses
A Distinguished Scholar in Database Systems and Data Science Education
Raymond Chi-Wing Wong serves as Professor and Associate Head (Education) in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology, where he has established himself as a leading expert in databases, data mining, and data science. After completing his BSc, MPhil, and PhD degrees from the Chinese University of Hong Kong in 2002, 2004, and 2008 respectively, he has made significant contributions to both research and education. His teaching excellence has been recognized through numerous awards, including the prestigious Michael G. Gale Medal for Distinguished Teaching in 2020, multiple Honorary Mentions in the HKUST Common Core Teaching Excellence Award, and the School of Engineering Teaching Excellence Award. His teaching philosophy, which he calls "being an alchemist," encompasses nine principles: Active interaction, Listening, Care, High-quality teaching, Eagerness to take challenges, Motivating students, Inspiring students, Sharing, and Technology. Beyond his teaching achievements, he has published over 123 conference papers and 48 journal articles in prestigious venues, with research contributions spanning privacy-preserving data publishing, spatial databases, and graph algorithms. His work has garnered significant attention in the academic community, with several of his papers receiving best paper awards at major conferences including SIGMOD and VLDB. As Program Director of the Undergraduate Research Opportunities Program, he continues to inspire and mentor the next generation of computer scientists while maintaining active research collaborations with leading institutions worldwide.
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