基于视觉原理的密度聚类算法

在模式识别、图像处理、聚类分析等领域，人的眼睛具有快速有效地组织并发现物体内部结构的自然能力，本文就是在模拟人类视觉系统这一功能的基础上，结合基于密度的聚类方法提出了一种新的聚类算法，该算法具有对初始化参数不敏感、能发现任意形状的聚类及能找到最优聚类等优点。

Density-based Clustering Method by Simulating Visual Systems

In pattern recognition, image processing and cluster analysis, human eyes possess a singular aptitude to group objects and find the important structures in an efficient and effective way. In this paper we propose a new data clustering approach by simulating the ability of human eyes based on density-based methods. This algorithm is insensitive to initialization, and can discover clusters of arbitrary shape and find optimal clustering.