Abstract
Video Association Mining(VAM) is the process of discovering associations in a given video. Two key phases of VAM are (i) Transformation and (ii) Frequent Temporal Pattern Mining. The transformation phase converts the original input video to an alternate transactional format, namely a cluster sequence. Frequent temporal pattern mining phase concerns the generation of patterns subject to the temporal distance and support thresholds. The paper addresses the issue of frequent temporal pattern mining and studies algorithms for the same. The existing Apriori based algorithm is compared with three other approaches highlighting the case specific situations suited by each.
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SivaSelvan, B., Gopalan, N.P. (2007). Efficient Algorithms for Video Association Mining. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_22
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DOI: https://doi.org/10.1007/978-3-540-72665-4_22
Publisher Name: Springer, Berlin, Heidelberg
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