CAJ | 학술논문

K-means算法因简单、高速等特点而被广泛应用，但该算法仍然存在依赖于初始聚类中心、易陷入局部最优等缺陷。为此，提出了一种蜜蜂交配优化聚类算法。该算法利用密度和距离初始化蜂群，并将局部搜索能力较强的粗糙集聚类算法作为工蜂的一种编码，以增强算法的局部搜索能力，最后在迭代过程中不断引入随机种群，增加种群的多样性，提高算法的全局寻优能力。实验结果表明，该算法不仅能有效抑制早熟收敛，而且具有较强的稳定性，较好的聚类效果。
K-means산법인간단、고속등특점이피엄범응용，단해산법잉연존재의뢰우초시취류중심、역함입국부최우등결함。위차，제출료일충밀봉교배우화취류산법。해산법이용밀도화거리초시화봉군，병장국부수색능력교강적조조집취류산법작위공봉적일충편마，이증강산법적국부수색능력，최후재질대과정중불단인입수궤충군，증가충군적다양성，제고산법적전국심우능력。실험결과표명，해산법불부능유효억제조숙수렴，이차구유교강적은정성，교호적취류효과。
K-means algorithm is the most widely used method due to its easy understanding and fast speed .However,this method has the disadvantage that the clustering results depend on the selection of the initial clustering center and it is easy to fall into local optimal .For this reason,this paper proposed a honey-bee mating optimization clustering algorithm .It generates initial swarm by density and distance,and regards rough set clustering algorithm which has strong local search ability as a code of the works to en-hance the local search ability of the algorithm .At last,in order to improve the diversity level of the swarm and the global optimiza-tion ability of the algorithm,random swarm population are introduced continuously in the iterative process .Our experiments show that the proposed algorithm not only can effectively suppress premature convergence,but also has strong stability and produces good clustering results .