Robust verification and analysis of the pre-clustering algorithm with a-priori non-specification of the number of clusters - Mosorov, Volodymyr; Panskyi, Taras; Biedron, Sebastian (Eastern-European Journal of Enterprise Technologies, 2015)
The range of the implementation of cluster analysis is wide, it extends from many technical applications to different branches of science, such as biology, medicine, computer sciences and psychology. The main purpose of the cluster analysis is dividing the investigated objects into homogeneous groups, or clusters, according to certain criteria and investigating the process of natural grouping of these objects. It means solving the task of grouping data and revealing in them a relevant structure. The unsupervised learning methods (clusterization) as opposed to the supervised learning methods (classification), marks of output objects, that is, determining each object belonging to the certain cluster, as well as the number of the clusters are not given from the very beginning of the process. The created clustering algorithm without a-priori information about the number of the clusters belongs to the group of pre-clustering algorithms. Pre-clustering is the procedure of checking the possibility of clustering the input data. Checking this possibility answers the question whether data can be divided into more than one cluster. The process of verification of the parameters of the pre-clustering algorithm concerns testing the rule of decision making for different types of input data. The selected cases of input data considered here are the input data with the normal distribution law having been grouped into one or two clusters.