CAJ | 학술논문

FCM算法对初始聚类中心敏感，对噪声和孤立点敏感，容易受到数据分布的影响。本文的改进算法引入物理学上的数据场理论，用势函数来描述数据的分布，优化初始聚类中心；同时采用冗余聚类中心的方法，即将大簇分割成多个小类，再用分离度作为评估函数进行类合并。仿真实验结果表明，改进算法能够克服FCM算法的一些缺陷，对数据分布不规则的数据集进行有效聚类，聚类效果良好。
FCM산법대초시취류중심민감，대조성화고립점민감，용역수도수거분포적영향。본문적개진산법인입물이학상적수거장이론，용세함수래묘술수거적분포，우화초시취류중심；동시채용용여취류중심적방법，즉장대족분할성다개소류，재용분리도작위평고함수진행류합병。방진실험결과표명，개진산법능구극복FCM산법적일사결함，대수거분포불규칙적수거집진행유효취류，취류효과량호。
The fuzzy c-means algorithm has several limitations:too sensitive to choose initial class center of divisions , too sensi-tive to noises and outliers , easy to be effected by data distributions .The improved algorithm uses the data field according to the theory of fields in physics , uses potential values of fault points in the data field to identify noise point and determine the initial class center , uses multicenters clustering algorithm that big cluster is cut into several small clusters , and then takes the separation measures as evaluation function to merge small clusters .Experiments show that the improved fuzzy c-means algorithm could make up the defects of fuzzy c-means algorithm , and be well suited to the non-uniform subject distributions .