CAJ | 학술논문

针对移动网络中用户的移动、数据丢失、用户定位不精确导致的用户挖掘准确性低的问题,提出了一种基于卡尔曼滤波的噪声数据纠正与丢失数据补偿的移动用户群挖掘算法,提高空问与时间上具有相关性的移动用户群挖掘的有效性.详细的仿真实验以及与现有的基于距离的移动用户群挖掘算法DMUM的对比表明,该算法不仅具有更少的执行时间,同时有效地提高了移动用户群挖掘的召回率与准确率.
침대이동망락중용호적이동、수거주실、용호정위불정학도치적용호알굴준학성저적문제,제출료일충기우잡이만려파적조성수거규정여주실수거보상적이동용호군알굴산법,제고공문여시간상구유상관성적이동용호군알굴적유효성.상세적방진실험이급여현유적기우거리적이동용호군알굴산법DMUM적대비표명,해산법불부구유경소적집행시간,동시유효지제고료이동용호군알굴적소회솔여준학솔.
To overcome the low accuracy caused by mobility,missing data and imprecise location information,a mobile user group mining algorithm is proposed based on Kalman filter to correct noise data and fill up missing data.The proposed algorithm improves the effectiveness of spatial-temporal correlative user mining.Detailed simulation results and comparisons with the existed Distance-based Mobile User Mining(DMUM) algorithm show that the proposed algorithm is not only with less execution time,but also greatly improves recall and precision of mobile user mining.