SA-BFSN：adaptive algorithm based on density clustering

Abstract

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）

摘要： 针对BFSN算法需要人工输入参数r和[λ]的缺陷，提出了一种自适应确定r和[λ]的SA-BFSN聚类方法。该方法通过Inverse Gaussian拟合判断r参数，通过分析噪声点数量的分布特征选择合适的[λ]值。算法测试表明，使用SA-BFSN无需人工输入参数，能够实现聚类过程的全自动化，能够有效处理任意形状、大小和密度的簇。

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）

CHEN Hao, HOU Huiqun, YANG Chengzhi, QIU Lei. SA-BFSN：adaptive algorithm based on density clustering[J]. Computer Engineering and Applications, 2012, 48(36): 186-189.

陈昊，侯慧群，杨承志，邱磊. SA-BFSN：一种自适应基于密度聚类的算法[J]. 计算机工程与应用, 2012, 48(36): 186-189.

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SA-BFSN：adaptive algorithm based on density clustering

SA-BFSN：一种自适应基于密度聚类的算法

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