ABSTRACT
In many cases, traditional cluster analysis, hierarchical clustering, is used for clustering time series. The aim of cluster analysis is to assign units to clusters so that units within each cluster are similar to one another with respect to observed variables, and the clusters themselves stand apart from one another. In other words, the goal is to divide the units into homogeneous and distinct clusters. Non-hierarchical clustering Hierarchical clustering groups data with a sequence of nested partitions, either from singleton clusters to a cluster including all individuals or vice versa. Non-hierarchical clustering directly divides data points into some pre-specified number of clusters without the hierarchical structure. The merging of a pair of clusters or the formation of a new cluster is dependent on the definition of the distance function between two clusters. Single linkage, complete linkage, and average linkage consider all points of a pair of clusters when calculating their inter-cluster distance, and they are also called graph methods.
