一种变栅格高维数据收缩聚类算法  

A Shrinking-Clustering Method for High Dimensional Data Using Flexible Size Grid

作　　者：张建业[1] 潘泉[1] 梁建海[2] 

机构地区：[1]西北工业大学自动化学院,西安710072 [2]空军工程大学工程学院,西安710038

出　　处：《模式识别与人工智能》2007年第5期716-721,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60304004);陕西省自然科学基础研究计划项目(No.2005F52)资助

摘　　要：针对数据挖掘领域中高维数据的聚类问题,提出一种变栅格的高维数据收缩聚类算法.主要是对数据箱按密度跨距进行排列,将数据点沿着密度梯度进行移动,产生浓缩聚类.采用大小可变的栅格对相连密度单元进行检测,当边界线不再改变时得到最优聚类结果.仿真结果表明,收缩聚类方法对低维、高维数据的聚类都具有良好效果.A shrinking-clustering method using flexible size grid is proposed to solve the clustering problem of high dimensional data in data mining. The data bins are arranged according to their density span, and the data points are moved along the direction of the density gradient. Thus the condensed and widely-separated clusters are generated. Then the connected components of dense cells are detected using a sequence of grids with flexible size. Finally, the best clustering result is obtained when the borderline does not change again. The simulation result shows that the method could detect clusters effectively and efficiently in both low and high dimensional data.

关 键 词：收缩聚类 密度跨距 变栅格 数据箱 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]