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
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Cluster Analysis in Data Mining
This course is part of Data Mining Specialization
Instructor: Jiawei Han
42,495 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(406 reviews)
16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
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Assessments
7 assignments
Taught in English
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This course is part of the Data Mining Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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.
What's included
1 video3 readings1 assignment1 discussion prompt1 plugin
What's included
13 videos2 readings2 assignments
What's included
15 videos3 readings1 assignment1 programming assignment
What's included
9 videos2 readings2 assignments
What's included
10 videos1 reading1 assignment1 programming assignment
In the course conclusion, feel free to share any thoughts you have on this course experience.
What's included
1 discussion prompt1 plugin
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4.1 (25 ratings)Offered by
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