Multi-Dimensional Banded Pattern Mining



Abdullahi, Fatimah and Coenen, frans ORCID: 0000-0003-1026-6649
(2018) Multi-Dimensional Banded Pattern Mining. .

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Abstract

Techniques for identifying “banded patterns” in n-Dimensional (n-D) zero-one data, so called Banded Pattern Mining (BPM), are considered. Previous work directed at BPM has been in the context of 2-D data sets; the algorithms typically operated by considering permutations which meant that extension to n-D could not be easily realised. In the work presented in this paper banding is directed at the n-D context. Instead of considering large numbers of permutations the novel approach advocated in this paper is to determine banding scores associated with individual indexes in individual dimensions which can then be used to rearrange the indexes to achieve a “best” banding. Two variations of this approach are considered, an approximate approach (which provides for efficiency gains) and an exact approach.

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 08 Jun 2018 10:42
Last Modified: 19 Jan 2023 01:32
DOI: 10.1007/978-3-319-97289-3_12
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3022333