文章基本信息

标题：Efficient Parallelism for Mining Sequential Rules in Time Series Data: A Lattice Based Approach*
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作者：Biplab Kumer Sarker ; Kuniaki Uehara
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：7A
页码：137-143
出版社：International Journal of Computer Science and Network Security
摘要：A parallel algorithm based on a lattice theoretic approach is proposed to find the rules among patterns that sustain sequential nature in the multi-stream time series data. The human motion data captured by motion capturing system considered as multi-stream multidimensional data is used as real time data set. The data set is transformed into sequences of symbols of lower dimension due to its complex nature. The relations among symbols are expressed as “rules”. The proposed algorithm is implemented on a Distributed Shared Memory (DSM) multiprocessors system. The experimental results justify the efficiency of finding rules from the sequences of the patterns for time series data by achieving significant speed up comparing with the previous reported algorithm.
关键词：Data Mining, Time series data, Parallel algorithm, Association rule and Multiprocessor system