Cluster Analysis in Data Mining

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.

• Understand basic concepts and methods of cluster analysis
• Study typical clustering algorithms including k-means, BIRCH, and DBSCAN/OPTICS
• Apply clustering validation and quality assessment techniques
• Recognize real-world applications of cluster analysis
• Evaluate clustering model performance and interpret results effectively

• No deep prior experience required
• Basic computer and internet skills helpful
• Ability to read English instructions and complete short practices