Pattern Discovery in Data Mining
Master data mining techniques for pattern discovery, from basic methodologies to advanced applications in text, spatial, and temporal data analysis.
Master data mining techniques for pattern discovery, from basic methodologies to advanced applications in text, spatial, and temporal data analysis.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Mining Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Master frequent pattern mining algorithms and techniques
Implement sequential pattern mining methods
Analyze spatial and temporal data patterns
Develop text mining and phrase discovery skills
Apply pattern discovery to real-world applications
Understand pattern evaluation measures
Skills you'll gain
This course includes:
4.3 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course covers both fundamental and advanced concepts in pattern discovery for data mining. Students learn essential methodologies for mining frequent patterns, sequential patterns, and spatial-temporal patterns. The curriculum includes practical applications in text mining, software bug analysis, and image pattern discovery. Special focus is given to modern algorithms like ToPMine and SegPhrase for quality phrase mining, and advanced topics including privacy-preserving pattern mining and pattern discovery in data streams.
Course Orientation
Module 1 · 1 Hours to complete
Module 1
Module 2 · 5 Hours to complete
Module 2
Module 3 · 2 Hours to complete
Module 3
Module 4 · 2 Hours to complete
Module 4
Module 5 · 6 Hours to complete
Fee Structure
Instructor
Michael Aiken Chair
Jiawei Han is the Michael Aiken Chair Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, where he leads the Data Mining Research Group. His research focuses on data mining, text mining, and intelligent systems, contributing significantly to the fields of machine learning and knowledge discovery. He has authored several influential books, including Machine Learning and Knowledge Discovery for Engineering Systems Health Management and Mining Software Specifications: Methodologies and Applications. Dr. Han's work is widely recognized, with numerous publications and citations in academic literature. He is actively involved in teaching and mentoring students in advanced computer science topics related to data mining and information systems.
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Frequently asked questions
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