Types of Cluster Analysis
There are a number of approaches to cluster analysis, including:
Centroid - clusters are represented by a central vector, which may not necessarily be a member of the data set; and example is k-means clustering
Density - clusters are defined as areas of higher density than the remainder of the data set
Distribution - based on probability distributions; clusters are defined as objects belonging most likely to the same distribution
Grid - analysis is performed on grid cells
Hierarchical - based on the core idea of objects being more related to nearby objects than to objects farther away
Cluster Algorithm Analysis
There are a number of methods for evaluating how well a clustering has been performed. One example is the Davies-Bouldin Index.