Paper Info
Title
Efficiently mining regional outliers in spatial data
Abstract
With the increasing availability of spatial data in many applications, spatial clustering and outlier detection have received a lot of attention in the database and data mining community. As a very prominent method, the spatial scan statistic finds a region that deviates (most) significantly from the entire dataset. In this paper, we introduce the novel problem of mining regional outliers in spatial data. A spatial regional outlier is a rectangular region which contains an outlying object such that the deviation between the non-spatial attribute value of this object and the aggregate value of this attribute over all objects in the region is maximized. Compared to the spatial scan statistic, which targets global outliers, our task aims at local spatial outliers. We introduce two greedy algorithms for mining regional outliers, growing regions by extending them by at least one neighboring object per iteration, choosing the extension which leads to the largest increase of the objective function. Our experimental evaluation on synthetic datasets and a real dataset demonstrates the meaningfulness of this new type of outliers and the greatly superior efficiency of the proposed algorithms.
Year
DOI
Venue
2007
10.1007/978-3-540-73540-3_7
SSTD
Keywords
Field
DocType
data mining community,spatial clustering,regional outlier,spatial data,outlying object,local spatial outlier,spatial regional outlier,aggregate value,rectangular region,neighboring object,greedy algorithm,objective function,outlier detection,data mining,delaunay triangulation
Spatial analysis,Anomaly detection,Data mining,Pattern recognition,Computer science,Outlier,Greedy algorithm,Artificial intelligence,Cluster analysis,Scan statistic,Delaunay triangulation
Conference
Volume
ISSN
Citations 
4605
0302-9743
3
PageRank 
References 
Authors
0.40
20
3
Name
Order
Citations
PageRank
Richard Frank1759.59
Wen Jin275335.48
Martin Ester39391858.89