计算机系统应用
計算機繫統應用
계산궤계통응용
APPLICATIONS OF THE COMPUTER SYSTEMS
2015年
4期
171-175
,共5页
Hubness%初始中心%最大最小距离方法%高维数据%聚类
Hubness%初始中心%最大最小距離方法%高維數據%聚類
Hubness%초시중심%최대최소거리방법%고유수거%취류
Hubness%initial center%maximin method%high-dimensional data%clustering
针对基于Hub的聚类算法K-hubs算法存在对初始聚类中心敏感的问题,提出一种基于Hub的初始中心选择策略。该策略充分利用高维数据普遍存在的Hubness现象,选择相距最远的K个Hub点作为初始的聚类中心。实验表明采用该策略的K-hubs算法与原来采用随机初始中心的K-hubs算法相比,前者拥有较好的初始中心分布,能够提高聚类准确率,而且初始中心所在的位置倾向于接近最终簇中心,有利于加快算法收敛。
針對基于Hub的聚類算法K-hubs算法存在對初始聚類中心敏感的問題,提齣一種基于Hub的初始中心選擇策略。該策略充分利用高維數據普遍存在的Hubness現象,選擇相距最遠的K箇Hub點作為初始的聚類中心。實驗錶明採用該策略的K-hubs算法與原來採用隨機初始中心的K-hubs算法相比,前者擁有較好的初始中心分佈,能夠提高聚類準確率,而且初始中心所在的位置傾嚮于接近最終簇中心,有利于加快算法收斂。
침대기우Hub적취류산법K-hubs산법존재대초시취류중심민감적문제,제출일충기우Hub적초시중심선택책략。해책략충분이용고유수거보편존재적Hubness현상,선택상거최원적K개Hub점작위초시적취류중심。실험표명채용해책략적K-hubs산법여원래채용수궤초시중심적K-hubs산법상비,전자옹유교호적초시중심분포,능구제고취류준학솔,이차초시중심소재적위치경향우접근최종족중심,유리우가쾌산법수렴。
K-hubs is a Hub-based clustering algorithm that is very sensitive to initialization. Therefore, this paper proposes an initialization method based on Hub to solve this problem. The initialization method takes full use of the feature of the Hubness phenomenon by selecting initial centers that are the most remote Hub points with each other. The experimental results show that compared with the random initialization of ordinary K-hubs algorithm, the proposed initialization method can obtain a better distribution of initial centers, which could enhance the clustering accuracy; moreover, the selected initial centers can appear near the cluster centers, which could speed up the convergence of the clustering algorithm.