Please use this identifier to cite or link to this item: https://doi.org/10.1007/11733836_13
Title: Mining outliers in spatial networks
Authors: Jin, W.
Jiang, Y.
Qian, W.
Tung, A.K.H. 
Issue Date: 2006
Source: Jin, W.,Jiang, Y.,Qian, W.,Tung, A.K.H. (2006). Mining outliers in spatial networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3882 LNCS : 156-170. ScholarBank@NUS Repository. https://doi.org/10.1007/11733836_13
Abstract: Outlier analysis is an important task in data mining and has attracted much attention in both research and applications. Previous work on outlier detection involves different types of databases such as spatial databases, time series databases, biomedical databases, etc. However, few of the existing studies have considered spatial networks where points reside on every edge. In this paper, we study the interesting problem of distance-based outliers in spatial networks. We propose an efficient mining method which partitions each edge of a spatial network into a set of length d segments, then quickly identifies the outliers in the remaining edges after pruning those unnecessary edges which cannot contain outliers. We also present algorithms that can be applied when the spatial network is updating points or the input parameters of outlier measures are changed. The experimental results verify the scalability and efficiency of our proposed methods. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41884
ISBN: 3540333371
ISSN: 03029743
DOI: 10.1007/11733836_13
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