Cluster Analysis in Data Mining

University of Illinois at Urbana-Champaign

data-analysis   |   Advanced   |   12 Hours

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.

What skills will you gain?

dbscan

cluster analysis

hierarchical clustering

Similarity Measure

c dynamic memory allocation

text mining

Algorithms

data clustering algorithms

analysis

Data Mining