Efficiently Mining Regional Outliers in Spatial Data

14 years 1 months ago
Efficiently Mining Regional Outliers in Spatial Data
With the increasing availability of spatial data in many applications, spatial clustering and outlier detection has 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 func...
Richard Frank, Wen Jin, Martin Ester
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where SSD
Authors Richard Frank, Wen Jin, Martin Ester
Comments (0)