CAJ | 학술논문

比重隶属度模糊聚类(FCPM)算法可从不同角度解决聚类问题,取得较好效果.协同聚类算法利用不同特征子集之间的协同关系,并与其它聚类算法相结合,可提高原有的聚类性能.文中在FCPM聚类算法的基础上进行改进,将其与协同聚类算法相结合,提出一种协同的FCPM聚类算法.该算法在原有FCPM聚类算法的基础上,提高对数据集的聚类效果.在对数据集Wine和Iris进行测试的结果表明,该方法优于FCPM算法,说明该方法的有效性.
비중대속도모호취류(FCPM)산법가종불동각도해결취류문제,취득교호효과.협동취류산법이용불동특정자집지간적협동관계,병여기타취류산법상결합,가제고원유적취류성능.문중재FCPM취류산법적기출상진행개진,장기여협동취류산법상결합,제출일충협동적FCPM취류산법.해산법재원유FCPM취류산법적기출상,제고대수거집적취류효과.재대수거집Wine화Iris진행측시적결과표명,해방법우우FCPM산법,설명해방법적유효성.
Fuzzy clustering with proportional membership(FCPM)algorithm can solve the clustering problem from different viewpoints.With the collaborative relations among different feature subsets,the collaborative fuzzy clustering is combined with other clustering algorithms to make its clustering result better than that of the one with the original algorithm.An improved fuzzy clustering algorithm is proposed based on the combination of FCPM and collaborative fuzzy clustering.The experimental results on the Wine and Iris datasets show the effectiveness of the proposed method.