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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This was my favorite course in the whole specialization. Everything is explained very concisely and clearly making the subject matter very easy to understand.
Good course for understanding the Cluster Analysis & Algorithms, instructor is very experienced and well explained, thanks
关于 数据挖掘 专项课程