CAJ | 학술논문

在分析归纳原有聚类方法不足的基础上，结合粗糙理论和模糊理论，给出了改进的粗糙模糊K-均值聚类算法；设计了新的模糊粗糙K-均值聚类算法，并验证了该聚类算法的有效性；进而将这两种聚类算法应用到支持向量机中，对训练样本做预处理，以减少样本数目，提高了其训练速度和分类精度。
재분석귀납원유취류방법불족적기출상，결합조조이론화모호이론，급출료개진적조조모호K-균치취류산법；설계료신적모호조조K-균치취류산법，병험증료해취류산법적유효성；진이장저량충취류산법응용도지지향량궤중，대훈련양본주예처리，이감소양본수목，제고료기훈련속도화분류정도。
The shortcomings of the original clustering methods are analyzed. Moreover, the rough theory and fuzzy theory are combined together. The improvement of rough fuzzy K-means clustering algorithm is given. A fuzzy rough K-means clustering algorithm is designed, and the validity of fuzzy rough K-means clustering algorithm is verified. The proposed clustering algorithms are applied to support vector machine. In the above applications, the training samples are pre-processed to reduce the number of samples and improve the training speed and the classification accuracy.