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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6.2 Clustering Evaluation Measuring Clustering Quality
Skills You'll Learn
Algorithms, Data Analysis, Exploratory Data Analysis, Data Validation, Data Mining, Applied Machine Learning, Machine Learning Algorithms, Statistical Methods, Unsupervised Learning
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 4
Taught By
Jiawei Han
Abel Bliss Professor