A Bottom Up Algorithm for Mining Spatiotemporal Patterns
The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of Spatiotemporal Patterns. This area can be defined as efficiently discovering interesting patterns from large data sets. The discoven. of hidden periodic patterns in spatiotemporal data could provide unveiling important information to the data analyst. In many applications that track and analyze spatiotemporal data, movements obey periodic patterns: the objects follow the same routes (approximately) over regular time intervals. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. Through this approach, we retrieved periodic patterns that are not frequent in the whole history, but during a continuous subinterval of it.
data mining eriodic patterns patiotemporal data
Sadiq Hussain G.C. Hazarika
System Administrator, Examination Branch Dibrugarh University, Dibrugarh, India Director i/c, Centre for Computer Studies Dibrugarh University. Dibrugarh, India
国际会议
海口
英文
151-155
2011-07-15（万方平台首次上网日期，不代表论文的发表时间）