Cluster analysis partitions the set of observations into mutually exclusive groupings in order to best represent distinct sets of observations within the sample. Cluster analysis is not able, however, to confirm the validity of these groupings. The researcher must do this by 1. ensuring that theoretical justification exists for the cluster analysis, and 2. following up by profiling and discriminating between groups. Cluster analysis identifies and classifies objects individuals or variables on the basis of the similarity of the characteristics they possess. Data may be thought of as points in a space where the axes correspond to the variables.
Cluster analysis divides the space into regions characteristic of groups that it finds in the data.
The discovery of white dwarfs and red giants is a famous example of cluster analysis. Stars were plotted by astronomers Hertzsprung and Russell according to the two features, log luminosity and log temperature. Three clusters emerged, white dwarfs, red giants and the main sequence in between them. Objectives of Cluster Analysis discovering types reducing the number of cases by enabling consideration of several types instead of numerous records 6. The data may need to be preprocessed by outlier detection and standardization.