欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
在k-平面聚类（kPC）算法的基础上，通过引入模糊隶属关系，提出模糊k-平面聚类（FkPC）算法．与kPC类似，FkPC同样从原型选择的角度出发，以k个超平面替代传统的点（类中心）作为聚类原型．同时，由于模糊隶属度的引入，FkPC更能体现各样本点和与之对应的聚类平面的隶属关系．在人工数据集和标准数据集上的实验，均证实了FkPC算法的聚类有效性．更深入地揭示出除相似性度量之外，原型表示对聚类结果同样有着至关重要的影响．
英文摘要：
A clustering algorithm named Fuzzy k-Plane Clustering （FkPC） is proposed by introducing fuzzy membership into the prevalent k Plane Clustering （kPC）. From the view of prototype selection, FkPC substitutes hyperplanes for points as the prototype, which is similar with kPC. Meanwhile, FkPC represents the membership between the points and its central hyperplanes much more clearly than kPC, due to the introduction of fuzzy membership into its objective function. Experimental results of both artificial and UCI datasets have proved the clustering validity of FkPC, and they also reveal that besides the similarity metric, the expression of prototype also plays a crucial role in clustering.
同期刊论文项目
同项目期刊论文
期刊信息
位置：