SOMSO: A self-organizing map approach for spatial outlier detection with multiple attributes

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外文摘要：In this paper， we propose a self-organizing map approach for spatial outlier detection， the SOMSO method。 Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood。 Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems。 Self-Organizing map (SOM) is an effective method for visualization and cluster of high dimensional data。 It can preserve intrinsic topological and metric relationships in datasets。 The SOMSO method can solve high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features。 The experimental results for the dataset based on U。S。 population census indicate that SOMSO approach can successfully be applied in complicated spatial datasets with multiple attributes。

外文关键词：

data analysisself-organising feature mapsSOMSOabnormal data pointsattribute informationdata clusteringdata miningdata visualizationmultiple attributenonspatial attribute valuesself-organizing mapspatial distributionspatial outlier detection

作者：

Qiao Cai、Haibo He、Hong Man

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作者单位：

Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA

会议名称：

International Joint Conference on Neural Networks;IJCNN 2009

会议地点：

Atlanta, GA(US);Atlanta, GA(US)

会议母体文献：

Neural Networks, 2009. IJCNN 2009

页码：

425-431

出版时间：

2009

DOI：

10.1109/IJCNN.2009.5178884