Contents
What is group clustering?
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
What is Cluster observation?
Use Cluster Observations to join observations that share common characteristics into groups. Cluster observations uses a hierarchical procedure to form the groups. At each step, two groups (clusters) are joined, until only one group contains all the observations at the final step.
All observations are then associated to the closest cluster center and new centers are computed as the mean of the observations of a given cluster. The observations are grouped with respect to the new centers iteratively until convergence; that is, no difference occurs in the next iteration.
How does clustering work in a data set?
Often, clustering involves sorting observations into groups without any prior idea on what the groups are (or, in machine learning jargon, without any labels, hence the unsupervised nature). These groups are delineated so that members of a group should be more similar to one another than they are to members of a different group.
What are the different types of clustering techniques?
There are four types of clustering techniques with relevance to toxicologic pathology. In hierarchical techniques, classes are subclassified into groups, with the process being repeated at several levels to produce a tree, with the defined groups as branches. Related groups arise from a common branch.
How are clusters classified in a statistical analysis?
Cluster analysis comprises several statistical classification techniques in which, according to a specific measure of similarity (see Section 9.9.7 ), cases are subdivided into groups (clusters) so that the cases in a cluster are very similar to one another and very different from the cases in other clusters.