摘要
大多数数据库都不能有效地处理数据的时间维度,时空同现模式挖掘有利于提取隐含在时空数据集中有价值的信息,目前已经成为研究热点。针对现有同现模式发现方法挖掘效率较低的问题,采用双层网络对时空数据进行初始化建模,针对传统方法在进行时空兴趣度计算时未考虑对象类型存在有效周期的问题,改进了现有兴趣度计算方法,引入了权重特征值,并提出了基于网络的时空同现模式挖掘算法。实验表明,在使用不同数据量的测试集中挖掘同现模式集时,新算法的运行效率优于不对数据集进行建模的方法以及仅对实例层进行建模的方法。
Most databases cannot effectively deal with time dimension of data,the spatial-temporal co-occurrence pattern mining is helpful to extract implicit valuable information from large spatio-temporal dataset,and it has become a hot research topic at present.To overcome lower mining efficiency of current co-occurrence pattern discovery methods,a double-level network model was used to initialize spatio-temporal dataset.In the calculation of spatial-temporal interestingness,traditional methods ignore the fact that every object-type has effective lifecycle.Thus,the current computation of interestingness was improved in this paper.We introduced weight eigenvalue and proposed a new spatial-temporal cooccurrence pattern mining algorithm based on network.Experiment results show that the proposed algorithm is more effective to calculate co-occurrence patterns in test sets with different data volumes than the methods without modeling or modeling instance layer only.
出处
《计算机科学》
CSCD
北大核心
2018年第3期223-230,共8页
Computer Science
基金
国家自然科学基金项目(61371143)
北方工业大学基于内容感知的最优图像缩放技术研究与应用科研平台(XN054)
北方工业大学优势学科项目(XN078)
太原科技大学校博士科研启动基金(20162036)资助
关键词
同现模式
时空关系网络
时空兴趣度
有效周期
Co-occurrence pattern
Spatial-temporal relation network
Spatial-temporal interestingness
Effective lifecycle
