Cluster Analysis in Data Mining
Master clustering methodologies and algorithms for data mining, from k-means to hierarchical methods and density-based approaches.
Master clustering methodologies and algorithms for data mining, from k-means to hierarchical methods and density-based approaches.
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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What you'll learn
Understand and apply various clustering methodologies
Implement partitioning and hierarchical clustering algorithms
Use density-based and grid-based clustering methods
Evaluate clustering quality using validation measures
Apply clustering techniques to real-world applications
Skills you'll gain
This course includes:
4 Hours PreRecorded video
7 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores cluster analysis in data mining, covering fundamental concepts and methodologies. Students learn various clustering techniques including partitioning methods like k-means, hierarchical methods such as BIRCH, and density-based approaches like DBSCAN/OPTICS. The curriculum includes methods for clustering validation and evaluation, with practical applications and case studies. Through hands-on programming assignments, participants gain practical experience in implementing clustering algorithms and validation measures.
Course Orientation
Module 1 · 1 Hours to complete
Module 1
Module 2 · 2 Hours to complete
Week 2
Module 3 · 5 Hours to complete
Week 3
Module 4 · 2 Hours to complete
Week 4
Module 5 · 4 Hours to complete
Course Conclusion
Module 6 · 25 Minutes 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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