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Clustering healthcare processes with a robust approach

Matsatsinis Nikolaos, Doumpos, Michael, Grigoroudis, Evangelos, Panagiotis Manolitzas, Pavlos Delias

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Summary

Process Mining has been proved to be an effective approach to discover process models from event logs. However, when a highly diversified behavior is allowed (like in a healthcare environment) mining a single model could yield pointless results. One way to tackle this diversity is clustering the log into coherent groups. In this work we propose a robust spectral clustering approach. The spectral approach let one with greater flexibility in representing data (a similarity measure is enough) while the proposed robust technique can handle outliers, a common occurrence in healthcare processes.

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