SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining top-n local outliers in large databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Deviants in a Time Series Database
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
Mining Deviants in Time Series Data Streams
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Clustering objects on a spatial network
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Outlier Detection Using k-Nearest Neighbour Graph
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
AutoPart: parameter-free graph partitioning and outlier detection
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A methodology for analyzing SAGE libraries for cancer profiling
ACM Transactions on Information Systems (TOIS)
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Navigational path privacy protection: navigational path privacy protection
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering moving objects in spatial networks
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.