关键词: 数据挖掘, 密度聚类, 基于广度优先搜索邻居的聚类算法（BFSN）, 自适应基于广度优先搜索邻居的聚类算法（SA-BFSN）

Abstract: Algorithm for BFSN defects that require manual input parameters r and [λ], an adaptive SA-BFSN clustering method that can automatically determine r and [λ] is proposed. The method determines r by Inverse Gaussian fitting parameters, and by analyzing the distribution of the number of noise points to select the appropriate value of [λ]. Algorithm tests show that use of SA-BFSN doesn’t need human input parameters, to achieve full automation of the clustering process, to deal effectively with any shape, size and density of the cluster.

Key words: data mining, density clustering, Broad First Search Neighbors（BFSN）, Self-Adaptive Broad First Search Neighbors（SA-BFSN）