CAJ | 학술논문

传统的k_means算法将欧式距离作为最常用的距离度量方法.针对基于欧式距离计算样本点与类间相似度的不足,用"相对距离"代替"绝对距离"可以更好地反映样本的实际分布,提出一种在领域知识未知的情况下基于加权欧式距离的k_means算法.针对公共数据库UCI里的数据实验表明改进后的算法能产生质量较高的聚类结果.
전통적k_means산법장구식거리작위최상용적거리도량방법.침대기우구식거리계산양본점여류간상사도적불족,용"상대거리"대체"절대거리"가이경호지반영양본적실제분포,제출일충재영역지식미지적정황하기우가권구식거리적k_means산법.침대공공수거고UCI리적수거실험표명개진후적산법능산생질량교고적취류결과.
Euclid distance is commonly used to measure distance in the traditional k_means algorithm. The k_ means algorithm based on weighted Euclid distance is researched and presented to overcome the existing prob-lems of similarity calculation in clustering analysis based on traditional Euclid distance when we have no any domain knowledge about the data objects, the relative distance but not absolute distance is more accurately re-sponse to data distribution. Experiments on the standard database UCI show that the proposed method can pro-duce a high accuracy clustering result.