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- W2215500401 abstract "A mechanism for identifying bandings in large zero-one N-dimensional data sets, using a sampling technique, is presented. The challenge of identifying bandings in data is the large number of potential permutations that need to be considered. To circumvent this a banding score mechanism is proposed that avoids the need to consider large numbers of permutations. This has been incorporated into a proposed banded pattern mining algorithm, the Exact ND Banded Pattern Mining (END BPM) algorithm. Although this operates well on reasonably sized datasets, there is still a challenge with respect to large N-dimensional data sets that cannot be held in primary storage. To this end a sampling technique is also proposed. The approach is fully described and evaluated using the GB cattle movement database, a real life database that records all movements of cattle in GB." @default.
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- W2215500401 date "2015-10-01" @default.
- W2215500401 modified "2023-09-22" @default.
- W2215500401 title "Finding banded patterns in big data using sampling" @default.
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- W2215500401 doi "https://doi.org/10.1109/bigdata.2015.7364012" @default.
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