Mining Spatio-Temporal Co-location Patterns with Weighted Sliding Window
Spatial co-location patterns represent the subsets of features (co-location) whose events are frequently located together in geographic space. Spatio-temporal co-location (cooccurrence) pattern mining extends the mining task to the scope of both space and time. However, embedding the time factor into spatial colocation pattern mining process is a subtle problem. Previous researches either treat the time factor as an alternative dimension or simply carry out the mining process on each time segment. In this paper, we propose a weighted sliding window model (WSWModel) which introduces the impact of time interval between the spatio-temporal events into the interest measure of the spatio-temporal co-location patterns. We figure out that the aforementioned two approaches fit into the two special cases in our proposed model. We also propose an algorithm (STCP-Miner) to mine spatio-temporal co-location patterns. The experimental evaluation with both the synthetic data sets and a real world data set shows that our algorithm is relatively effective with different parameters.
Feng Qian Liang Yin Qinming He Jiangfeng He
College of Computer Science and Technology,Zhejiang University,Hangzhou,China
国际会议
上海
英文
1996-2000
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）