Low-support, High-correlation Mining

    Abstract

    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)

    Topics

    2 Figures and Tables