Cluster Evolution Analytics

Víctor Morales-Oñate and Bolívar Morales-Oñate

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we propose Cluster Evolution Analytics (CEA) as a framework that can be considered in the realm of Advanced Exploratory Data Analysis or unsupervised learning. CEA leverages on the temporal component of panel data and it is based on combining two techniques that are usually not related: leave-one-out and plug-in principle. This allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. We illustrate our results on a real dataset applying CEA on different clustering algorithms and developed a Shiny App with a particular configuration. Finally, we also provide an R package so that this framework can be used on different applications.

Keywords: clustering; temporal clustering; statistical profiles (search for similar items in EconPapers)
JEL-codes: C02 C38 C63 (search for similar items in EconPapers)
Date: 2024-02-19
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:120220

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