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Large boolean matrices are a basic representational unit in a variety of applications, with some notable examples being interactive visualization systems, mining large graph structures, and association rule mining. Designing space and time efficient scalable storage and query mechanisms for such large matrices is a challenging problem. We present a lossless compression strategy to store and access such large matrices efficiently on disk. Our approach is based on viewing the columns of the matrix as points in a very high dimensional Hamming space, and then formulating an appropriate optimization problem that reduces to solving an instance of the Traveling Salesman Problem on this space. Finding good solutions to large TSP's in high dimensional Hamming spaces is itself a challenging and little-explored problem -- we cannot readily exploit geometry to avoid the need to examine all N2 inter-city distances and instances can be too large for standard TSP codes to run in main memory. Our multi-faceted approach adapts classical TSP heuristics by means of instance-partitioning and sampling, and may be of independent interest. For instances derived from interactive visualization and telephone call data we obtain significant improvement in access time over standard techniques, and for the visualization application we also make significant improvements in compression.