Clusters
Advances in systems analysis, software engineering, and high performance computing book series · 2017
Cluster analysis, which we approach in this chapter, is the task of grouping a set of objects in such a way that objects in the same group or cluster are more similar to each other than to those in other groups or clusters. It is a common technique for statistical data analysis. Cluster analysis can be achieved by various algorithms that might differ significantly. Therefore, cluster analysis as such is not a trivial task. It is an interactive multi-objective optimization that involves trial and error. Therefore, in cluster analysis, the clustering of subjects or variables are made from similarity measures or dissimilarity (distance) between two subjects initially, and later between two clusters. These groups can be done using hierarchical or non-hierarchical techniques.