基于网格的快速搜寻密度峰值的聚类算法优化研究

CFSFDP是基于密度的新型聚类算法,可聚类非球形数据集,具有聚类速度快、实现简单等优点。然而该算法在指定全局密度阈值dc时未考虑数据空间分布特性,导致聚类质量下降,且无法对多密度峰值的数据集准确聚类。针对以上缺点,提出基于网格分区的CFSFDP(简称GbCFSFDP)聚类算法。该算法利用网格分区方法将数据集进行分区,并对各分区进行局部聚类,避免使用全局dc,然后进行子类合并,实现对数据密度与类间距分布不均匀及多密度峰值的数据集准确聚类。两个典型数据集的仿真实验表明,GbCFSFDP算法比CFSFDP算法具有更加精确的聚类效果。

Optimization of grid based clustering by fast search and find of density peaks

The CFSFDP is a clustering algorithm based on density peaks, which can cluster arbitrary shape data sets, and has the advantages of fast clustering and simple realization. However, the global density threshold dc, which can lead to the decrease of clustering quality, is specified without the consideration of spatial distribution of the data. Moreover, the data sets with multi-density peaks cannot be clustered accurately. To resolve the above shortcomings, we propose an optimized CFSFDP algorithm based on grid (GbCFSFDP). To avoid the using of global dc, the algorithm divides the data sets into smaller partitions by using the grid partitioning method and performs local clustering on them. Then the GbCFSFDP merges the sub classes. Data sets, which are unevenly distributed and have multi-density peaks, are correctly classified. Simulation experiments of two typical data sets show that the GbCFSFDP algorithm is more accurate than the CFSFDP.