Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
An Interweaved HMM/DTW Approach to Robust Time Series Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Spectral Clustering and Embedding with Hidden Markov Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Model-based clustering of array CGH data
Bioinformatics
Sequence Mining for Business Analytics: Building Project Taxonomies for Resource Demand Forecasting
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
A novel HMM-based clustering algorithm for the analysis of gene expression time-course data
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
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We describe a exible framework for biomedical time series clustering that aims to facilitate the use of temporal information derived from EHRs in a meaningful way. As a case study, we use a dataset indicating the presence of physician ordered glucose tests for a population of hospitalized patients and aim to group individuals with similar disease status. Our approach pairs Hidden Markov Models (HMMs) to abstract variable length temporal information, with non-parametric spectral clustering to reveal inherent group structure. We focus on systematically comparing the performance of our approach with two alternative clustering methods that use various time series statistics instead of HMM based temporal features. Intrinsic evaluation of cluster quality shows a dramatic improvement using the HMM based feature set, generating clusters that indicate more than 90% of patients are similar to members of their own cluster, and distinct from patients in neighboring clusters.