Abstract
A tremendous growing interest in finding dependency among patterns has been developing in the domain of time series data mining. It is quite effective to find how current and past values in the streams of data are related to the future. However, these kind of data sets with high dimensionality are enormous in size results in possibly large number of mined dependencies. This strongly motivates the need of efficient parallel algorithms. In this paper, we propose two parallel algorithms to discover dependency from the large amount of time series data. We introduce the method of extracting sequence of symbols from the time series data by using segmentation and clustering processes. To reduce the search space and speed up the process we investigate the technique to group the time series data. The experimental results conducted on a shared memory multiprocessors system justifies the inevitability of using parallel techniques for mining huge amount of data in the time series domain.
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Sarker, B.K., Mori, T., Hirata, T., Uehara, K. (2003). Parallel Algorithms for Mining Association Rules in Time Series Data. In: Guo, M., Yang, L.T. (eds) Parallel and Distributed Processing and Applications. ISPA 2003. Lecture Notes in Computer Science, vol 2745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-37619-4_28
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DOI: https://doi.org/10.1007/3-540-37619-4_28
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