摘要
针对可能性聚类对初始化参数设置依赖性较强的问题,提出一种基于中心自动融合的可能性聚类算法,并证明了算法中尺度因子的多尺度性质.该算法通过建立中心的相关性判定准则,根据数据自身分布特点动态调整聚类数目与结构,通过引入尺度参数实现对数据的多分辨率分析.与传统的模糊和可能性聚类算法相比,该算法摆脱了对聚类数目及初始化中心或隶属度矩阵设置的依赖性,易于控制.人造数据和真实数据实验结果表明,该算法能自动确定数据中不同尺度下的聚类结构,具有识别不同大小聚类结构的能力.
To deal with the parameter sensitivity problem of possibilistic c-means clustering algorithm, a new possibilistic clustering algorithm based on center merging was proposed.The cluster number and structure were dynamically adj usted according to the data distribution.The algorithm has the ability to execute multi-scale analysis task for the given data set by means of adj usting the values of the scale factor.The theorems were also given that were proven to be used to analyze the multi-scale property of the algorithm.Compared with the traditional fuzzy or possibilistic clustering algorithms, the proposed algorithm avoids its dependence on the initial conditions of centers,cluster number and membership matrix,which makes it easy to control.Synthetic and real data experimental results show that the algorithm can be used to detect the cluster structures of the data set from different scales,and to find the clusters with different sizes.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2014年第1期86-92,共7页
Journal of Jilin University:Science Edition
基金
国家科技支撑计划项目(批准号:2009BAE69B02)
吉林省教育厅"十二五"科学技术研究项目(批准号:2013-420)
吉林农业大学科研启动基金(批准号:201135)
长春理工大学青年基金(批准号:XQNJJ-2011-10)
关键词
可能性聚类
多尺度
中心融合
初始化敏感性
possibilistic clustering
multi-scale
center merging
initialization sensitivity
