CAJ | 학술논문

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。
침대가능성취류대초시화삼수설치의뢰성교강적문제，제출일충기우중심자동융합적가능성취류산법，병증명료산법중척도인자적다척도성질。해산법통과건립중심적상관성판정준칙，근거수거자신분포특점동태조정취류수목여결구，통과인입척도삼수실현대수거적다분변솔분석。여전통적모호화가능성취류산법상비，해산법파탈료대취류수목급초시화중심혹대속도구진설치적의뢰성，역우공제。인조수거화진실수거실험결과표명，해산법능자동학정수거중불동척도하적취류결구，구유식별불동대소취류결구적능력。
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.