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This paper presens a mechanism for finding banded patterns on largezero-one NDimensional data using segmentation technique. Traditionally, banding problem requires the generation of permutations. In this paper, Banded Pattern Mining (BPM) algorithms have been used, the BPM approximate and BPM exact. BPM algorithm incorporate abanding Score mechanism that does not consider large number of permutations. Although these algorithms operates well in sizable N-D datasets, large N-D dataset that cannot be stored easily on computer internal memory still present a challenge. To this end, a segmentation technique for discovering banding in large data compatible with the BPM is proposed. The technique was evaluated using a real life datasets the Great Britain (GB) cattle tracing system that represents the movements of all cattle in GB. From the reported evaluations, the mechanism was able to identify bandings in zero-one N-D datasets using series of data segmentstaken from a large N-D datasetwithin shortest possible time. The result shows BPM exact algorithm as the most effective in terms of overall banding result and BPM approximate algorithm as the most efficient in terms of runtime. [ABSTRACT FROM AUTHOR]