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.
Cluster Analysis in Data Mining
This course is part of Data Mining Specialization
Instructor: Jiawei Han
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There are 6 modules in this course
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.
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1 video3 readings1 assignment1 discussion prompt1 plugin
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13 videos2 readings2 assignments
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15 videos3 readings1 assignment1 programming assignment
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9 videos2 readings2 assignments
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10 videos1 reading1 assignment1 programming assignment
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