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Computer-aided Diagnosis via Hierarchical Density Based Clustering

Tom Obry 1 Louise Travé-Massuyès 1 Audine Subias 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : When applying non-supervised clustering, the concepts discovered by the clustering algorithm hardly match business concepts. Hierarchical clustering then proves to be a useful tool to exhibit sets of clusters according to a hierarchy. Data can be analyzed in layers and the user has a full spectrum of clusterings to which he can give meaning. This paper presents a new hierarchical density-based algorithm that advantageously works from compacted data. The algorithm is applied to the monitoring of a process benchmark, illustrating its value in identifying different types of situations , from normal to highly critical.
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Submitted on : Monday, July 23, 2018 - 4:13:03 PM
Last modification on : Thursday, June 10, 2021 - 3:01:40 AM
Long-term archiving on: : Wednesday, October 24, 2018 - 3:58:59 PM


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  • HAL Id : hal-01847563, version 1


Tom Obry, Louise Travé-Massuyès, Audine Subias. Computer-aided Diagnosis via Hierarchical Density Based Clustering. 29th International Workshop on Principles of Diagnosis (DX 2018), Aug 2018, Varsovie, Poland. 8p. ⟨hal-01847563⟩



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