Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
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
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
GLS-SOD: a generalized local statistical approach for spatial outlier detection
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust refinement methods for camera calibration and 3D reconstruction from multiple images
Pattern Recognition Letters
Spatial outlier detection: data, algorithms, visualizations
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Outlier detection concerns discovering some unusual data whose behavior is exceptional compared to other data. In contrast to non-spatial outliers which only consider non-spatial attributes, spatial outliers are defined to be those sites which are very different from its neighbors defined in terms of spatial attributes, i.e., locations. In this paper, we propose a local trimmed mean approach to evaluating the spatial outlier factor which is the degree that a site is outlying compared to its neighbors. The structure of our approach strictly follows the general spatial data model, which states spatial data consist of trend, dependence and error. We empirically demonstrate trimmed mean is more outlier-resistant than median in estimating sample location and it is employed to estimate spatial trend in our approach. In addition to using the 1st order neighbors in computing error, we also use higher order neighbors to estimate spatial trend. With true outlier factor supposed to be given by the spatial error model, we compare our approach with spatial statistic and scatter plot. Experimental results on two real datasets show our approach is significantly better than scatter plot, and slightly better than spatial statistic.