IterativeSOMSO: An iterative self-organizing map for spatial outlier detection
Document Type
Conference Proceeding
Date of Original Version
7-15-2010
Abstract
In this paper, we propose an iterative self-organizing map approach for spatial outlier detection (IterativeSOMSO). IterativeSOMSO method can address high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features. Detection of spatial outliers facilitates further discovery of spatial distribution and attribute information for data mining problems. The experimental results indicate our proposed approach can be effectively implemented for the large spatial dataset based on U.S. Census Bureau with approving performance. © 2010 Springer-Verlag.
Publication Title, e.g., Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
6063 LNCS
Issue
PART 1
Citation/Publisher Attribution
Cai, Qiao, Haibo He, Hong Man, and Jianlong Qiu. "IterativeSOMSO: An iterative self-organizing map for spatial outlier detection." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6063 LNCS, PART 1 (2010): 325-330. doi: 10.1007/978-3-642-13278-0_42.