Abstract
In the past years, data has become increasingly fast and volatile, making the ability to track its evolution an highly significant part of the value extraction process. In this work we present a framework to monitor evolution of clusters and present its use on real world data. We develop a framework over a previous one by Oliveira and Gama from 2013. Its biggest contribution is the addition of the concept of control area. This area will create a region around the cluster where it is still possible to establish associations with clusters from other time points. It aims to expand the search scope for cluster associations while diminishing the number of false positives. Changes to the transition definitions and detection algorithm are also introduced to accommodate the existence of this area. We demonstrate this framework at work in a real world scenario testing it with a telecom industry dataset and make a detailed analysis of the obtained results.
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Pereira, G., Mendes-Moreira, J. (2016). Monitoring Clusters in the Telecom Industry. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Mendonça Teixeira, M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-31307-8_65
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DOI: https://doi.org/10.1007/978-3-319-31307-8_65
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