基于数据分区的最近邻优先聚类算法

A Data-Partitioning-Based Nearest-Neighbors-First Clustering Algorithm

聚类是数据挖掘领域的一个重要研究方向。最近邻优先吸收(NNAF)算法可以快速进行聚类并且能有效处理噪声点,但当数据密度和聚类间的距离不均匀时聚类质量较差。本文在分析NNAF算法不足的基础上,提出了一种基于数据分区的NNAF 算法-PNNAF 算法,较好地改善了聚类质量。

Clustering is an important research direction in the field of Data Mining. This paper analyses the Nearest Neighbors Absorbed First （NNAF） clustering algorithm. This algorithm can cluster quickly with noisy . However, clustering quality will degrade when the cluster density and distance between clusters are not even. In this paper,a Nearest-Neighbors-First clustering algorithm based on data partitioning is proposed. The new algorithm improves the quality of clustering.