Subpopulation Discovery in Epidemiological Data with Subspace Clustering

• Autorzy: Niemann U , Spiliopoulou M. , Völzke H , Kühn J-P

Identyfikatory

DOI

10.2478/fcds-2014-0015

• Języki publikacji: EN

Abstrakty

EN

A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2014
• Tom: Vol. 39, No. 4
• Strony: 271--300
• Opis fizyczny: Bibliogr. 46 poz., rys., tab.

Twórcy

autor

Niemann U

• Otto-von-Guericke University Magdeburg, Germany

autor

Spiliopoulou M.

• Otto-von-Guericke University Magdeburg, Germany

autor

Völzke H

• University Medicine Greifswald, Germany

autor

Kühn J-P

• University Medicine Greifswald, Germany

Bibliografia

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