Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for ...Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.展开更多
为监测分布式驱动电动汽车中轮毂电机运行状态,确保整车运行安全,提出一种基于改进的多类支持向量数据描述(multi-class support vector data description,简称MCSVDD)的轮毂电机故障诊断方法。首先,针对MCSVDD算法的改进,基于近邻传播(...为监测分布式驱动电动汽车中轮毂电机运行状态,确保整车运行安全,提出一种基于改进的多类支持向量数据描述(multi-class support vector data description,简称MCSVDD)的轮毂电机故障诊断方法。首先,针对MCSVDD算法的改进,基于近邻传播(affinity propagation,简称AP)聚类算法提出了MCSVDD以“距离类内簇中心最小”的类别判断法则,并基于Weibull函数构造了Weibull核函数,用于优化数据描述模型;其次,针对轮毂电机运行状态的多维特征参数组,提出一种基于最小距离传播鉴别投影(minimum-distance propagation discriminant projection,简称MPDP)的降维法,提高了不同工况下轮毂电机故障状态的可分性;最后,定制带有典型轴承故障的轮毂电机,采集7种工况下的振动信号,验证所提出方法的有效性。结果表明:基于MPDP降维后的轮毂电机运行状态观测样本的可分性优于线性判别分析(linear discriminant analysis,简称LDA)、局部保持投影(locality preserving projection,简称LPP)及最小距离鉴别投影(minimum-distance discriminant projection,简称MDP)方法,基于Weibull核函数的MCSVDD状态识别系统的识别精度整体高于基于多项式和高斯核函数的MCSVDD系统。展开更多
针对单一最小支持度挖掘关联规则不能反应不同数据项出现频度与性质的问题,提出了一个基于频繁模式树的多重支持度关联规则挖掘算法MSDMFIA(Multiple minimum Supports for Discover Maximum Fre-quent Item sets Algorithm),根据不同...针对单一最小支持度挖掘关联规则不能反应不同数据项出现频度与性质的问题,提出了一个基于频繁模式树的多重支持度关联规则挖掘算法MSDMFIA(Multiple minimum Supports for Discover Maximum Fre-quent Item sets Algorithm),根据不同数据项的特点定义多重支持度,通过挖掘数据库中的最大频繁项目集,计算最大频繁候选项目集在数据库中的支持度来发现关联规则.该算法可以解决关联规则挖掘中经常出现的稀少数据项问题,并解决了传统的关联规则挖掘算法中的生成频繁候选集和多次扫描数据库的性能瓶颈.实验结果表明,本文提出的算法在功能和性能方面均优于已有算法.展开更多
基金TheNationalHighTechnologyResearchandDevelopmentProgramofChina (No .86 3 5 11 930 0 0 9)
文摘Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
文摘针对单一最小支持度挖掘关联规则不能反应不同数据项出现频度与性质的问题,提出了一个基于频繁模式树的多重支持度关联规则挖掘算法MSDMFIA(Multiple minimum Supports for Discover Maximum Fre-quent Item sets Algorithm),根据不同数据项的特点定义多重支持度,通过挖掘数据库中的最大频繁项目集,计算最大频繁候选项目集在数据库中的支持度来发现关联规则.该算法可以解决关联规则挖掘中经常出现的稀少数据项问题,并解决了传统的关联规则挖掘算法中的生成频繁候选集和多次扫描数据库的性能瓶颈.实验结果表明,本文提出的算法在功能和性能方面均优于已有算法.
