Abstract
The concept of Banded pattern mining is concerned with the identification of “bandings” within zero-one data. A zero-one data set is said to be fully banded if all the “ones” can be arranged along the leading diagonal. The discovery of a banded pattern is of interest in its own right, at least in a data analysis context, because it tells us something about the data. Banding has also been shown to enhances the efficiency of matrix manipulation algorithms. In this paper the exact N dimensional Banded Pattern Mining (BPM) algorithm is presented together with a full evaluation of its operation. To illustrate the utility of the banded pattern concept a case study using the Great Britain (GB) Cattle movement database is also presented.
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Abdullahi, F.B., Coenen, F., Martin, R. (2015). Finding Banded Patterns in Data: The Banded Pattern Mining Algorithm. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_8
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DOI: https://doi.org/10.1007/978-3-319-22729-0_8
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