A new approach to effective circuit clustering
ICCAD '92 1992 IEEE/ACM international conference proceedings on Computer-aided design
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Unified Approach to Detecting Spatial Outliers
Geoinformatica
Algorithms for Spatial Outlier Detection
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Detecting region outliers in meteorological data
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
Proceedings of the 2004 ACM symposium on Applied computing
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
FS3: A Random Walk Based Free-Form Spatial Scan Statistic for Anomalous Window Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Outlier Detection Using Random Walks
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Detecting distance-based outliers in streams of data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Spatial Outlier Detection: A Graph-Based Approach
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Spatial outlier detection: data, algorithms, visualizations
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Data-driven trajectory smoothing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Data Mining and Knowledge Discovery
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A spatial outlier is a spatially referenced object whose non-spatial attributes are very different from those of its spatial neighbors. Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed. However, in these techniques, there exist the problems of masking and swamping. That is, some spatial outliers can escape the identification, and normal objects can be erroneously identified as outliers. In this paper, two Random walk based approaches, RW-BP (Random Walk on Bipartite Graph) and RW-EC (Random Walk on Exhaustive Combination), are proposed to detect spatial outliers. First, two different weighed graphs, a BP (Bipartite graph) and an EC (Exhaustive Combination), are modeled based on the spatial and/or non-spatial attributes of the spatial objects. Then, random walk techniques are utilized on the graphs to compute the relevance scores between the spatial objects. Using the analysis results, the outlier scores are computed for each object and the top k objects are recognized as outliers. Experiments conducted on the synthetic and real datasets demonstrated the effectiveness of the proposed approaches.