An Effective Method for Mining Quantitative Association Rules with Clustering Partition in Satellite Telemetry Data
The correlation analysis of telemetry data plays a significant role in satellite performance analysis.However,the existing methods cannot be well applied,because the telemetry data is large and high-dimensional.In this paper,an efficient algorithm named QARC_Apriori is proposed.First,to reduce the redundant attributes and lower the problem complexity,grey relational analysis method is applied.Second,each filtered attribute is partitioned into several subintervals,combining with K-Means clustering algorithm.During clustering,the outliers are removed to improve the accuracy of clustering results.Due to different distributions and scopes of attributes,the clustering centers are automatically adjusted.Moreover,the statistical information of each attribute is used to avoid repeatedly scanning database.Finally,all quantitative association rules are mined by an improved Apriori algorithm.In order to improve the mining efficiency,two pruning strategies are used.The experiments are conducted with the power supply data of a Chinas satellite from 2011.6.1 to 2011.9.1.It indicates that the proposed algorithm is suitable for quantitative association rules mining and is important for satellite on-orbit performance analysis.
Telemetry data Quantitative association rules Clustering Grey correlation analysis Discretization
Xin Dong Dechang Pi
College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing,China
国际会议
2014 2nd International Conference on Advanced Cloud and Big Data (CBD 2014)(2014年先进云计算和大数据国际会议)
安徽黄山
英文
26-33
2014-11-20（万方平台首次上网日期，不代表论文的发表时间）