文献类型： 外文期刊

作者： Wan, You ¹ ; Zhou, Jiaogen ² ; Bian, Fuling ¹ ;

作者机构： 1.Wuhan Univ, Res Ctr Spatial Informat & Digital Engn, Int Sch Software, Wuhan 430079, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100101, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr informat Technol Agr, Beijing 100097, Peoples R China

期刊名称：FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS

ISSN：

年卷期： 2008 年

页码：

收录情况： SCI

摘要： Spatial co-location and de-location patterns represent subsets of Boolean spatial feature types whose instances are often located in close/separate geographic proximity. Existing literatures pay more attention on mining co-location patterns based on distance threshold spatial relation. In this paper, we proposed a novel co-location and de-location patterns mining algorithm (CODEM) to discover useful co-location and de-location patterns in large spatial datasets. We used k nearest features (k-NF) to measure spatial close/separate relationships of co-location/de-location patterns in spatial datasets. The k-NF set of one feature type's instances was used to evaluate the close/separation relationship between other features and one feature. Then, a correlation checking operation was adopted to filter the uninteresting patterns, and moreover a grid index method was used to accelerate the k nearest features query, while a T-tree (Total support tree) structure was also used to compress the candidate frequent and infrequent item sets, and generate patterns efficiently. Experimental results prove that the algorithm is accurate and efficient, has a time complexity of O(n).