Low-support, High-correlation Mining


    We continue to assume a market-basket" model for data, and we visualize the data as a boolean matrix, where rows = baskets and columns = items. Key assumptions: 1. Matrix is very sparse; almost all 0's. 2. The number of columns items is suuciently small that we can store something per column in main memory, but suuciently large that we cannot store… (More)


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