Novel Feature-Based Clustering of Micro-Panel Data (CluMP)
This work addresses a limited supply of clustering methods for micro-panel data, offering an incremental improvement in efficiency for researchers and industry analysts.
The paper tackles the problem of clustering micro-panel data by proposing a novel two-step feature-based method called CluMP, which achieves similar or better clustering performance than existing methods while being more time-efficient for large datasets.
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.