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Efficient Clustering for Large-Scale, Sparse, Discrete Data with Low Fundamental Resolution

Scalable algorithm design has become central in the era of large-scale data analysis. My contribution to this line of research is the design of new algorithms for scalable clustering and data reduction, by exploiting inherent low-dimensional structure in the input data to overcome the challenges of significant amounts of missing entries. I demonstrate that, by focusing on a property of the data that we call its fundamental resolution, we can improve the efficiency of clustering methods on sparse, discrete-valued data sets.