摘要
空间聚类是空间数据挖掘中一个非常重要的方法.本文在分析 DBSCAN 算法不足的基础上,提出一种改进的空间聚类算法(AISCA).为了能够有效处理大规模空间数据库,算法采用一种新的抽样技术.另外,通过引入匹配邻域的概念,使得算法在聚类时不仅考虑空间属性也考虑非空间属性.二维空间数据测试结果表明算法是可行、有效的.
Spatial clustering is one of the most important spatial data mining techniques. In this paper, an improved spatial clustering algorithm (AISCA) based on DBSCAN is proposed. In order to cluster large-scale spatial databases effectively, the proposed algorithm adopts a new sampling technique. In addition, it considers not only spatial attributes but also non-spatial attributes by introducing the concept of the matching neighborhood. Experimental results of 2-D spatial datasets show that the proposed algorithm is feasible and efficient.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2007年第3期371-376,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60673127)
江苏省自然科学基金(No.BK2001045)
关键词
空间数据挖掘
空间聚类
非空间属性
Spatial Data Mining, Spatial Clustering, Non-Spatial Attributes