CAJ | 학술논문

针对传统聚类算法存在时间性能低效且需要输入参数的缺点，本文提出了一种自动连结链聚类新算法。该算法在确立数据的基础上，通过计算数据点与各顶点的距离并加以排序形成不同群组，然后快速搜寻出它们的相邻点形成连结链网络，再根据连结链的平均距离删除过长的连结链，从而达到聚类的目的。实验结果表明，本文算法与 DBSCAN 及 Single-Link 算法具有相同的聚类效果，但执行时间约仅为这两种算法的10％。
침대전통취류산법존재시간성능저효차수요수입삼수적결점，본문제출료일충자동련결련취류신산법。해산법재학립수거적기출상，통과계산수거점여각정점적거리병가이배서형성불동군조，연후쾌속수심출타문적상린점형성련결련망락，재근거련결련적평균거리산제과장적련결련，종이체도취류적목적。실험결과표명，본문산법여 DBSCAN 급 Single-Link 산법구유상동적취류효과，단집행시간약부위저량충산법적10％。
Time performance for shortcomings and inefficiencies of traditional clustering algorithms require in-put parameters,this paper proposes a new algorithm.The algorithm on the basis of the data side established by the distance calculation of data points and the vertices and be able to sort the formation of different groups, and then quickly find out their adjacent points form a network link chain,according to the average distance is too long and then delete the link chain link chain,which serve the purpose of clustering.Experimental results show that the execution time of the automatic link chain clustering algorithm accounts for about 10% of the common algorithm.