CAJ | 학술논문

为解决大规模数据集聚类过程中内存容量受限问题,提出了一种基于聚类个数约束的快速聚类算法,只需扫描一趟原始数据集,半径阈值随聚类过程动态变化;同时定义了一种包含分类属性取值频率信息的类间差异性度量,可用于混合属性数据集,时间复杂度与空间复杂度同数据集大小,属性个数近似成线性关系.在KDDCUP99数据集上的实验结果表明,提出的算法输入参数少,具有良好的聚类特性,可用于大规模数据集.
위해결대규모수거집취류과정중내존용량수한문제,제출료일충기우취류개수약속적쾌속취류산법,지수소묘일쟁원시수거집,반경역치수취류과정동태변화;동시정의료일충포함분류속성취치빈솔신식적류간차이성도량,가용우혼합속성수거집,시간복잡도여공간복잡도동수거집대소,속성개수근사성선성관계.재KDDCUP99수거집상적실험결과표명,제출적산법수입삼수소,구유량호적취류특성,가용우대규모수거집.
To solve the constraint of the memory capacity during clustering the large-scale dataset,a fast clustering algorithm based on the constraint of the number of clusters is put forward.The original dataset is read only once and the radius threshold changes dynamically.At the same time an inter-cluster dissimilarity measure taking into account the frequency information of the categorical attribute values is introduced,which can be used for the mixed dataset.The time complexity and space complexity are nearly linear with the size of dataset and the number of attributes.The experimental results on the KDDCUP99 dataset show that the proposed algorithm is feasible and effective,which can be used for the large-scale dataset.