Mining Frequent Moving Patterns of Objects in Sensor Networks

摘要：

Aiming at the issue of mining frequent moving patterns with two dimensional attributes including locations and time in sensor networks, a novel method named OMP-mine is proposed in this paper, OMP-mine is based on a novel data structure named OMP-tree and a scheme of conditional search. The OMP-tree can efficiently store large numbers of original moving patterns compactly and the method of conditional search can efficiently narrow the search space. OMP-mine adopts the idea of pattern growth, recursively fetches frequent prefix patterns from the conditional pattern bases directly, and joints the suffix to make a pattern grow. Simulation results show OMP-mine can efficiently discover frequent moving patterns with two dimensional attributes in sensor networks and decreases its complexity in time and space simultaneously.

关键词： data mining sensor networks object tracking

作者: Yuanguo Cheng Lujing Yang Qiyuan Li

作者单位: Electrical Engineering College of Navy Engineering University Wuhan 430033, China;Computer College o Electrical Engineering College of Navy Engineering University Wuhan 430033, China

会议类型: 国际会议

会议名称: 2006 IEEE International Conference on Information Acquisition

会议地点: 山东威海

会议语种:英文

页码: 797-801

在线出版日期: 2006-08-20（万方平台首次上网日期，不代表论文的发表时间）