Cluster analysis
Clustering or cluster analysis is a type of data analysis. The analyst groups objects so that objects in the same group (called a cluster) are more similar to each other than to objects in other groups (clusters) in some way. This is a common task in data mining.
Cluster Analysis Media
The result of a cluster analysis shown as the coloring of the squares into three clusters
Single-linkage on Gaussian data. At 35 clusters, the biggest cluster starts fragmenting into smaller parts, while before it was still connected to the second largest due to the single-link effect.
Single-linkage on density-based clusters. 20 clusters extracted, most of which contain single elements, since linkage clustering does not have a notion of "noise".
k-means separates data into Voronoi cells, which assumes equal-sized clusters (not adequate here).
k-means cannot represent density-based clusters.
On Gaussian-distributed data, EM works well, since it uses Gaussians for modelling clusters.
Density-based clusters cannot be modeled using Gaussian distributions.
DBSCAN assumes clusters of similar density, and may have problems separating nearby clusters.
OPTICS is a DBSCAN variant, improving handling of different densities clusters.
