Online Course
Cluster Analysis in Data Mining
- Provider Coursera
- Cost Free Online Course (Audit)
- Session In progress
- Language English
- Certificate Paid Certificate Available
- Effort 4-6 hours a week
- Duration 4 weeks long
- Learn more about MOOCs
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Overview
Class Central Tips
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.
-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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Reviews for Coursera's Cluster Analysis in Data Mining Based on 7 reviews
- 5 star 0%
- 4 stars 29%
- 3 star 14%
- 2 stars 43%
- 1 star 14%
Did you take this course? Share your experience with other students.
Write a review- 1
Gregory S
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Gregory
completed this course and found the course difficulty to be medium.
Cluster Analysis in Data Mining is third course in Coursera's new data mining specialization offered by the University of Illinois Urbana-Champaign. The course is a 4-week overview of data clustering: unsupervised learning methods that attempt to group data into clusters of related or similar observations....
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Bijaya
completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
I thought the class was good for someone who already knows how to apply clustering analysis to data. I have been using different clustering algorithms in the past, this class gave me a greater overview of the other clustering methods that existed that I hadn't been exposed to. I do not recommend this for someone who is new to the concept/application of clustering.
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Kristina Š
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Kristina
completed this course and found the course difficulty to be medium.
I liked the way I was able to learn more about the newest trends in clustering algorithms, but there was too much theory, and too little practice. However, it was a fun experience, but I hope in the second iteration that the ratio of the programming assignments and the theoretical descriptions of various algorithms and papers will be equal.
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