Abstract:
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This piece of work proposes a clustering approach based on Dirichlet process (DP) mixture model. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model has the ability to differentiate common and unique longitudinal patterns among observations, and jointly cluster subjects and variables. Via joint clustering, the intrinsic complex shared patterns among subjects and among variables are captured. The number of joint clusters and cluster assignments are self-controlled with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size allergic sensitization data is used to demonstrate the method.
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