Abstract
A banded pattern in “zero-one” high dimensional data is one where all the dimensions can be organized in such a way that the “ones” are arranged along the leading diagonal across the dimensions. Rearranging zero-one data so as to feature bandedness allows for the identification of hidden information and enhances the operation of many data mining algorithms that work with zero-one data. In this paper an effective ND banding algorithm, the ND-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 GB Cattle movement database is also presented.
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Abdullahi, F.B., Coenen, F., Martin, R. (2014). A Scalable Algorithm for Banded Pattern Mining in Multi-dimensional Zero-One Data. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_31
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DOI: https://doi.org/10.1007/978-3-319-10160-6_31
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