An Enhanced Streaming Pattern Discovery Algorithm for Sensor Networks
This paper proposes an enhanced pattern discovery algorithm for data streams processing of sensor networks, in order to improve the performance of SPIRIT. The new algorithm adapts the optimized correction for tracking weights vectors, and the dynamic expanding in detecting the number of hidden variables. Simulation results show that compared to the original algorithm, the proposed algorithm can reduce the reconstruction error, increase the energy fraction of reconstruction, and decrease the number of hidden variables, so it extract the principal components and discover patterns among streams more efficiently.
sensor networks data streams optimized correction dynamic expanding
Wei Cheng Haoshan Shi, Dong Li
School of Electronics and Information Northwestern Polytechnical University Xian, China
国际会议
成都
英文
441-445
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）