CAJ | 학술논문

为了降低谱聚类采用高斯函数作为相似性度量方式对参数的敏感性，以及能够发现多密度簇的同时降低噪声点的干扰，提出了一种将基于均衡接近度的灰关联分析结合到谱聚类中的新方法，采用加权的自适应相似性度量方式。最后用改进的FCM算法对其进行聚类。在真实数据集和人工数据集上分别对提出的算法和现有算法进行了比较分析。研究结果表明，提出的新算法能够消除参数的影响，具有更高的聚类精度。聚类精度采用F测度指标。
위료강저보취류채용고사함수작위상사성도량방식대삼수적민감성，이급능구발현다밀도족적동시강저조성점적간우，제출료일충장기우균형접근도적회관련분석결합도보취류중적신방법，채용가권적자괄응상사성도량방식。최후용개진적FCM산법대기진행취류。재진실수거집화인공수거집상분별대제출적산법화현유산법진행료비교분석。연구결과표명，제출적신산법능구소제삼수적영향，구유경고적취류정도。취류정도채용F측도지표。
Aiming to reduce sensitivity to parameter by using gaussian function as the similarity measure in spectral clustering and to identify multidimensional clusters with reducing the outliers in-terference, a new approach is presented, in which grey relational analysis based on the balanced closeness degree is integrated with spectral clustering and an adaptive weighted similarity measure is used, and finally the improved FCM algorithm is used for clustering.The proposed algorithm is com-pared with the existing algorithms on the artificial and real-world datasets.The experimental results demonstrate that the proposed algorithm can avoid the effect of parameter and achieve higher cluste-ring accuracy by using F measurement index.