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Subpopulation Discovery in Epidemiological Data with Subspace Clustering

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

Foundations of Computing and Decision Sciences

2014

Vol. 39, No. 4

271--300

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.

Tracing cluster transitions for different cluster types

Ntoutsi I., Spiliopoulou M., Theodoridis Y.

Control and Cybernetics

2009

Vol. 38, no 1

239-259

Clustering algorithms detect groups of similar population members, like customers, news or genes. In many clustering applications the observed population evolves and changes over time, subject to internal and external factors. Detecting and understanding changes is important for decision support. In this work, we present the MONIC+ framework for cluster-type-specific transition modeling and detection. MONIC+ encompasses a typification of clusters and cluster-type-specific transition indicators, by exploiting cluster topology and cluster statistics for the transition detection process. Our experiments on both synthetic and real datasets demonstrate the usefulness and applicability of our framework.