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DBSB:启发式选择边界对象的快速空间聚类算法

DBSB:fast spatial clustering method with heuristically selecting border object
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摘要 提出了一种启发式选择边界对象的快速空间聚类算法DBSB,通过一个启发式函数近似选择相对于某个已知核心对象边界区域中的核心对象和边界对象,通过核心对象的序列来快速地扩展它们所在的簇,直至找到一些较小的簇。在此基础上再通过边界对象快速地合并某些簇,即该算法通过两步聚类,达到最终的聚类。理论分析和实验结果表明该算法有效可行。 In this paper,a novel clustering algorithm DBSB （Density Based Spatial Clustering Method with Heuristically Selecting Border Object） is proposed.The algorithm fastly expands the clusters by a heuristical function to choose core objects in the border region of the known core object,and then merges some clusters by border objects.That is,the DBSB algorithm gets the ultimate clustering result through two steps of clustering.Finally,the theoretical analysis and experimental results indicate that the algorithm is effective and efficient.
作者 陶亮 倪志伟 刘晓
机构地区 合肥工业大学管理学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第11期164-167,共4页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.70471046) 安徽省自然科学基金(the Natural Science Foundation of Anhui Province of China under Grant No.050460402)。
关键词 空闻聚类 边界对象 密度 spatial clustering border object density
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献10

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计算机工程与应用
2007年 第11期

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