Density-Based spatial outliers detecting

Authors:
Tianqiang Huang;Xiaolin Qin;Chongcheng Chen;Qinmin Wang
Affiliations:
Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Spatial Information Research Center in Fujian Province, Fuzhou, China;Spatial Information Research Center in Fujian Province, Fuzhou, China
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Year:
2005

Citing 6
Cited 0

The R*-tree: an efficient and robust access method for points and rectangles

SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries

SIGMOD '97 Proceedings of the 1997 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
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

Data Mining and Knowledge Discovery
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Existing work in outlier detection emphasizes the deviation of non-spatial attribution not only in statistical database but also in spatial database. However, both spatial and non-spatial attributes must be synthetically considered in many applications. The definition synthetically considered both was presented in this paper. New Density-based spatial outliers detecting with stochastically searching approach (SODSS) was proposed. This method makes the best of information of neighborhood queries that have been detected to reduce many neighborhood queries, which makes it perform excellently, and it keeps some advantages of density-based methods. Theoretical comparison indicates our approach is better than famous algorithms based on neighborhood query. Experimental results show that our approach can effectively identify outliers and it is faster than the algorithms based on neighborhood query by several times.