A0264
Title: A hybrid mixture approach for clustering and characterizing cancer data
Authors: Kazeem Kareem - Michigan Technological University (United States)
Fan Dai - Michigan Technological University (United States) [presenting]
Abstract: Model-based clustering is widely used for identifying and distinguishing types of diseases. However, modern biomedical data, coming with high dimensions, make it challenging to perform the model estimation in traditional cluster analysis. The incorporation of factor analyzer into the mixture model provides a way to characterize the large set of data features, but the current estimation method is computationally impractical for massive data due to the intrinsic slow convergence of the embedded algorithms, and the incapability to vary the size of the factor analyzers, preventing the implementation of a generalized mixture of factor analyzers and further characterization of the data clusters. A hybrid matrix-free computational scheme is proposed to efficiently estimate the clusters and model parameters based on a Gaussian mixture, along with generalized factor analyzers to summarize the large number of variables using a small set of underlying factors. The approach outperforms the existing method with faster convergence while maintaining high clustering accuracy. The algorithms are applied to accurately identify and distinguish breast cancer based on large tumor samples, and to provide a generalized characterization for subtypes of lymphoma using massive gene records.
