ABSTRACT
Cluster analysis is a collection of data analysis techniques and procedures that help researchers to group, sort, classify and explore data to uncover or identify groups or structures within the data. Although cluster analysis has a long pre-digital history. The chapter discusses the technological developments in digital tools, software and visualisation packages, along with the increasing use of digital methods that use cluster analysis techniques and procedures, such as exploratory data mining. Cluster analysis is used by researchers working in a variety of disciplines and fields of study including economic development, psychiatry, health informatics, nursing research, medicine, customer profiling, education and welfare reform. The methods and techniques that are used for cluster analysis, including latent class cluster analysis, mixture model clustering, neural network-based clustering, overlapping clustering, partition clustering, partial clustering, pyramid clustering, simultaneous clustering, nearest neighbour clustering and mode clustering.
