Abstract
Spatial outliers are spatial objects whose non-spatial attributes are significantly different from the values of their neighborhoods. Detection of spatial outliers will provide the user with meaningful, interesting and potential information. Usually, algorithms for outlier mining on traditional business-oriented datasets are no longer applicable to spatial datasets. A new algorithm based on MST clustering is proposed in this paper to identify spatial outliers. The algorithm organically integrates the approach of minimum spanning trees and the density-based mechanism for outlier mining. Basic spatial structure characteristics of spatial objects are maintained by Delaunay Triangles and MST clustering is achieved by cutting off several most inconsistent edges. It turns out that the algorithm can find true spatial outliers, and it doesnt require any parameter for the algorithm be specified firstly. Experiments on real application problems indicate that the proposed algorithm is feasible and effective for identifying outliers from the large-scale spatial datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lu, W., Han, J., Oöi, B.C.: Discovery of General Knowledge in Large Spatial Databases. In: 1993 Far East Workshop on Geographic Information Systems (FEGIS 1993), Singapore, pp. 275–289 (1993)
Shekhar, S., Lu, C.T., Zhang, P.: A Unified Approach to Detecting Spatial Outliers. Geo-Informatica: An International Journal on Advances of Computer Science for Geographic Information System 7(2), 139–166 (2003)
Ng, R.T., Han, J.: Efficient clustering methods for spatial data mining. In: Proc. The 20th International Conference on Very Large Data Bases, Santiago, pp. 144–155 (1994)
Ester, M., Kriefel, H.P., Sander, J., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. The 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)
Lu, C.T., Chen, D.C., Kou, Y.F.: Algorithms for spatial outlier detection. In: Proceedings of the Third IEEE International Conference on Data Mining, pp. 597–600. Melbourne (2003)
Kou, Y., Lu, C.T., Chen, D.: Spatial weighted outlier detection. In: The 2006 SLAM Conference on Data Mining, Bethesda, Maryland, pp. 613–617 (2006)
Shekhar, S., Lu, C.T., Zhang, P.S.: Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, California, pp. 371–376 (2001)
Shekhar, S., Lu, C.T., Zhang, P.: Detecting graph-based spatial outlier. Intelligent Data Analysis: An International Journal 6(5), 451–468 (2002)
Lu, C.T., Chen, D.C., Kou, Y.F.: Detecting spatial outliers with Multiple Attributes. In: Proceeding of the 15th IEEE International Conference on tools with artificial intelligence, Sacramento, California, USA, pp. 122–128 (2003)
Wang, Z.Q., Wang, S.K., Hong, T., Wan, X.H.: A spatial outlier detection algorithm based multi-attributive correlation. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, China, pp. 1727–1732 (2004)
Wang, Z.Q., Li, J.H., Yu, H.Q., Chen, H.B.: Research of spatial outlier detection based on quantitative value of attributive correlation. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 5906–5910 (2006)
Ai, T.H.: Research on the supporting data model and methodology of city map database integration. Doctoral dissertation, Wuhan University (2000) (in Chinese)
Markus, M.B., Hans-Peter, K., Raymond, T.N., Jörg, S.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Dallas, Texas, USA, pp. 93–104 (2000)
Ma, X.H., Dong, J., Pan, Z.G., Shi, J.Y.: A Graph-Based Algorithm for Generating the Delaunay Triangulation of a Planar Point Set. China Journal of Image and Graphics 2(1), 7–11 (1997) (in Chinese)
Cui, G.Z., Cao, L.Z., Zhang, X.C., Wang, Y.F.: Research of Density-Based MST Clustering Algorithm. Computer Engineering and Applications 05, 155–158 (2006) (in Chinese)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, J., Ye, D., Chen, C., Gao, M. (2008). Minimum Spanning Tree Based Spatial Outlier Mining and Its Applications. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_69
Download citation
DOI: https://doi.org/10.1007/978-3-540-79721-0_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
Online ISBN: 978-3-540-79721-0
eBook Packages: Computer ScienceComputer Science (R0)