Novel Feature-Based Clustering of Micro-Panel Data (CluMP)

TL;DR

This paper introduces CluMP, a new feature-based clustering method for micro-panel data that offers comparable or better accuracy than existing methods, with improved time efficiency suitable for large datasets.

Contribution

The paper presents a novel two-step feature-based clustering approach specifically designed for micro-panel data, enhancing efficiency and performance.

Findings

CluMP achieves similar or better clustering accuracy compared to existing methods.

The proposed method is more time-efficient, suitable for large datasets.

CluMP performs well on simulated micro-panel data sets.

Abstract

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.