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.
2.5 Proximity Measure between Two Vectors Cosine Similarity
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您将学习的技能
Cluster Analysis, Data Clustering Algorithms, K-Means Clustering, Hierarchical Clustering
审阅
Apr 3, 2020
it was a really good experience. this course has given me good exposure to data mining
Sep 24, 2017
A very good course, it gives me a general idea of how clustering algorithm work.
从本节课中
Module 1
教学方
Jiawei Han
Abel Bliss Professor
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