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Cluster Analysis in Data Mining

Offered By: University of Illinois at Urbana-Champaign via Coursera

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Data Mining Courses Data Science Courses Data Analysis Courses Machine Learning Courses Cluster Analysis Courses K-means Courses

Course Description

Overview

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Syllabus

  • Course Orientation
    • You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
  • Module 1
  • Week 2
  • Week 3
  • Week 4
  • Course Conclusion
    • In the course conclusion, feel free to share any thoughts you have on this course experience.

Taught by

Jiawei Han

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