CAJ | 학술논문

为了克服噪声特征向量对聚类的影响,充分考虑各特征向量对聚类结果的贡献度的不同,运用mercer核将待聚类的数据映射到高维空间,提出了一种新的动态加权模糊核聚类算法.该算法运用动态加权,自动消弱噪声特征向量在分类中的作用,在对数据没有任何先验信息的情况下,不仅能够准确划分线性数据,而且能够做到非线性划分非团状数据.仿真和实际数据分类结果表明,数据中的噪声对分类结果影响较小,该算法具有很高的实用性.
위료극복조성특정향량대취류적영향,충분고필각특정향량대취류결과적공헌도적불동,운용mercer핵장대취류적수거영사도고유공간,제출료일충신적동태가권모호핵취류산법.해산법운용동태가권,자동소약조성특정향량재분류중적작용,재대수거몰유임하선험신식적정황하,불부능구준학화분선성수거,이차능구주도비선성화분비단상수거.방진화실제수거분류결과표명,수거중적조성대분류결과영향교소,해산법구유흔고적실용성.
In order to overcome the influence of noise feature vector on clustering, take full account of the different contribution of feature vector on clustering, a new fuzzy clustering algorithm with dynamic weights based on the kernel method is presented. The algorithm cluster data with noise features in high feature space mapped by the mercer kernel, not only can overcome the influence of noise feature vector on clustering, but also cluster the line and the non-group data without any experience, the clustered results of simulation data and real data show that this new algorithm may be used as a more accurate method in clustering data.