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.
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5.4 Grid-Based Clustering Methods
Skills You'll Learn
K-Means Clustering, Data Clustering Algorithms, Hierarchical Clustering, Statistical Programming, Machine Learning Algorithms, Machine Learning, Cluster Analysis, Python Programming
Reviews
4.5 (408 ratings)
- 5 stars65.93%
- 4 stars23.77%
- 3 stars5.63%
- 2 stars2.20%
- 1 star2.45%
VB
Nov 6, 2019
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
BK
Apr 3, 2020
it was a really good experience. this course has given me good exposure to data mining
From the lesson
Week 3
Taught By
Jiawei Han
Abel Bliss Professor