Spatial Neighborhood Clustering Based on Data Field

摘要：

Based on the theory of data field, each sample point in the spatial database radiates its data energy from the sample space to the mother space. This paper studies the use of the data field as a basis for clustering. We put forward a novel method for clustering, which is a kind of natural clustering method called spatial neighborhood clustering. In the data field, the potential center is identical to the cluster center. The key step of the cluster algorithm is to find the potential centers in the grid units of data field. The spatial neighborhood cluster method makes use of the distribution property of the potential value point as the potential center in the data field to discriminate the maximum potential value in a certain neighborhood window. Then the cluster centers can be acquired corresponding to the maximum potential values and the number of cluster centers is automatically amount to that of potential centers.

关键词： clustering spatial data mining data field spatial neighborhood discriminating

作者: Meng Fang Shuliang Wang Hong Jin

作者单位: International School of Software,Wuhan University Wuhan 430079 China State Key Laboratory of Software Engineering Wuhan University China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 262-269

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）