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.
Offered By
About this Course
Skills you will gain
- Cluster Analysis
- Data Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering
Offered by
University of Illinois at Urbana-Champaign
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Start working towards your Master's degree
Syllabus - What you will learn from this course
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.
Reviews
- 5 stars66.58%
- 4 stars23.29%
- 3 stars5.56%
- 2 stars2.02%
- 1 star2.53%
TOP REVIEWS FROM CLUSTER ANALYSIS IN DATA MINING
Very intense and required complex thinking and programming skill
A very good course, it gives me a general idea of how clustering algorithm work.
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
Covers great deal of topics and various aspects of clustering
About the Data Mining Specialization
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.