  Micro-panel data are collected and analysed in many research and industry
areas. Cluster analysis of micro-panel data is an unsupervised learning
exploratory method identifying subgroup clusters in a data set which include
homogeneous objects in terms of the development dynamics of monitored
variables. The supply of clustering methods tailored to micro-panel data is
limited. The present paper focuses on a feature-based clustering method,
introducing a novel two-step characteristic-based approach designed for this
type of data. The proposed CluMP method aims to identify clusters that are at
least as internally homogeneous and externally heterogeneous as those obtained
by alternative methods already implemented in the statistical system R. We
compare the clustering performance of the devised algorithm with two extant
methods using simulated micro-panel data sets. Our approach has yielded similar
or better outcomes than the other methods, the advantage of the proposed
algorithm being time efficiency which makes it applicable for large data sets.
