Description

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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The techniques and tools covered in Cluster Analysis in Data Mining are most similar to the requirements found in Business Analyst job advertisements.


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AlgorithmsCluster AnalysisData AnalysisData MiningUser Experience

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Coursera - University of Maryland, College Park