基于近邻方法的高维数据可视化聚类发现

提出了一种新颖的基于近邻方法的高维数据可经聚类方法，并实现了一个近邻可视化聚类发现系统ＶｉｓＮＮ。已有的解决高维数据可视化聚类方法主要是通过降维把维数据投影到二维或三维空间上，从而达到可视化目的。

VISUAL CLUSTERING FOR HIGH DIMENSIONAL DATA BASED ON NEAREST NEIGHBOR

A new visual clustering method for high dimensional data is presented, and a general visualization system VisNN is implemented in this paper. Classical visual method for clustering high dimension data is to project high dimensional data into two-dimension or three-dimension space by using dimension reduction method. In the method for each axis of two-dimension X-Y space, a record series, instead of a reduced attribute, are used. When the order of records is sorted by some attributes that users concern about, the distance relationship of two records in high dimensional space could be kept to some extent in the X-Y space. The experiments show that VisNN can help users discover interesting clusters and abnormality easily.