文献类型：期刊文献

英文题名：Mining at most top-K% spatio-temporal outlier based context: A summary of results

作者：Wang, Zhanquan[1]; Gu, Chunhua[1]; Ruan, Tong[1]; Duan, Chao[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2011

卷号：7003 LNAI

期号：PART 2

起止页码：688

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20114214443199)

语种：英文

外文关键词：Statistics - Data mining

摘要：Discovering STCOD is an important problem with many applications such as geological disaster monitoring, geophysical exploration, public safety and health etc. However, determining suitable interest measure thresholds is a difficult task. In the paper, we define the problem of mining at most top-K% STCOD patterns without using user-defined thresholds and propose a novel at most top-K% STCOD mining algorithm by using a graph based random walk model. Analytical and experimental results show that the proposed algorithm is correct and complete. Results show the proposed method is computationally more efficient than naive algorithms. The effectiveness of our methods is justified by empirical results on real data sets. It shows that the algorithms are effective and validate. ? 2011 Springer-Verlag.