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
Cluster Analysis in Data Mining
This course is part of Data Mining Specialization
Instructor: Jiawei Han
43,806 already enrolled
Gain insight into a topic and learn the fundamentals.
409 reviews
2 weeks to complete
at 10 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
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Showing 3 of 409
RG
Reviewed on Jan 24, 2021
The material is too general, does not provide examples. So it's difficult when doing the exam.
UG
Reviewed on Apr 27, 2019
Its Good but explanations can done much better, rest all good in terms of study material, quiz ,and programming assignment.
MU
Reviewed on Aug 26, 2023
A tough course regarding programming assignment and few quiz.
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