旋转网格:一种新的聚类融合方法

网格聚类以网格为单位学习聚簇，速度快、效率高。但它过于依赖密度阂值的选择，并且构造的每个聚簇边界呈锯齿状，不能很好地识别平滑边界曲面。针对该问题，提出一种新的面向网格问题的聚类融合算法(RG) . RG不是通过随机抽样数据集或随机初始化相关参数来创建有差异的划分，而是随机地将特征划分为K个子集，使用特征变换得到K个不同的旋转变换基，形成新的特征空间，并将网格聚类算法应用于该特征空间，从而构建有差异的划分。实验表明，RU能够有效地划分任意形状、大小的数据集，并能有效地解决网格聚类过分依赖于密度阂值选择以及边界处理过于粗糙的问题，其精度明显高于单个网格聚类。

Rotation Grid:A New Cluster Ensemble Method

Although it is rapid and efficient to use the grid-based clustering approach to learn the partition of a data set,grid clustering is excessively dependent on the initialization of density threshold, and the margin of each cluster constructed by the approach presents zigzag manner, which prohibits the recognition of smooth boundary surface. Thus, this paper proposed a new grid-oriented cluster ensemble approach(RG) to solve this problem. Instead of constructing the partitions with diversity on a given data set by random sampling or initializing parameters of corresponding algorithm,RG randomly splits the features set into K subsets,uses feature transformation method on the subsets to learn K differrent rotation basis,and applies grid cluster algorithm to the new feature space formed by the K axis rotations to learn the partitions with diversities. Experimental results show that, compared with single grid clustering, RG not only partilions the data set with arbitrary shape or size efficiently, but also alleviates its dependence on the density threshold initialization and smoothes the rough boundary.