@inproceedings{edea1eac6c3542ebb9ca356718c32901,
title = "IterativeSOMSO: An iterative self-organizing map for spatial outlier detection",
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.",
keywords = "Mahalanobis distance, Neural network, Self-organizing map, Spatial data mining, Spatial outlier",
author = "Qiao Cai and Haibo He and Hong Man and Jianlong Qiu",
year = "2010",
doi = "10.1007/978-3-642-13278-0_42",
language = "English",
isbn = "3642132774",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "325--330",
booktitle = "Advances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings",
edition = "PART 1",
note = "7th International Symposium on Neural Networks, ISNN 2010 ; Conference date: 06-06-2010 Through 09-06-2010",
}