Obstacle clustering and outlier detection
Author: 
Yong ShiAuthors Info & Claims
Yong ShiAuthors Info & ClaimsArticle No.: 88, Pages 1 - 2
Abstract
In this paper, we present our research on data mining approaches in the presence of obstacles. Many algorithms have been designed to detect clusters with obstacles in spatial databases. However, few considered to detect clusters and outliers simultaneously and interactively. Here we extend our original research on iterative cluster and outlier detection to study the problem of detecting cluster and outliers iteratively with the presence of obstacles. In many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing.
References
[1]
Anthony K. H. Tung, Jean Hou and Jiawei Han. Spatial clustering in the presence of obstacles. In ICDE '01: Proceedings of the 17th International Conference on Data Engineering, page 359, Washington, DC, USA, 2001. IEEE Computer Society.
[2]
V. Estivill-Castro and I. Lee. Autoclust: Automatic clustering via boundary extraction for mining massive point-data sets. In In Proceedings of the 5th International Conference on Geocomputation, pages 23--25, 2000.
[3]
V. Estivill-Castro and I. Lee. Autoclust+: Automatic clustering of point-data sets in the presence of obstacles. In TSDM '00: Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers, pages 133--146, London, UK, 2001. Springer-Verlag.
[4]
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI Press, 1996.
[5]
R. T. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases, pages 144--155, San Francisco, CA, USA, 1994. Morgan Kaufmann Publishers Inc.
[6]
O. Z. University and O. R. Zaïane. Clustering spatial data when facing physical constraints. In In Proc. of the IEEE International Conf. on Data Mining, pages 737--740, 2002.
[7]
X. Wang and H. J. Hamilton. Dbrs: A density-based spatial clustering method with random sampling. In PAKDD, pages 563--575, 2003.
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Published In
April 2010
488 pages
ISBN:9781450300643
DOI:10.1145/1900008
- Conference Chair:
- H. Conrad Cunningham,
- Program Chairs:
- Paul Ruth,
- Nicholas A. Kraft
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Published: 15 April 2010
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