Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP ...Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms.We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects.Leveraging the monotonicity that the clusters’number increases with the self-similarity in AP,we propose a model selection procedure that can determine the number of clusters adaptively.For the parameters introduced by extending AP in non-spherical clustering,we provide a grid-evolving strategy to optimize them automatically.The effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks.Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.展开更多
A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered...A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved.展开更多
为贯彻落实新发展理念,助力实现双碳目标,文章对农村地区电动货车充电站选址进行了研究。运用以相似度矩阵为基础,依靠消息传递迭代更新的近邻传播(affinity propagation,AP)聚类算法,从现有乡镇货运站中筛选出电动货车充电站候选点。...为贯彻落实新发展理念,助力实现双碳目标,文章对农村地区电动货车充电站选址进行了研究。运用以相似度矩阵为基础,依靠消息传递迭代更新的近邻传播(affinity propagation,AP)聚类算法,从现有乡镇货运站中筛选出电动货车充电站候选点。在多个属性权重信息未知的情况下,采用基于离差最大化的区间直觉模糊优劣解距离法(technique for order preference by similarity to an ideal solution,TOPSIS)得到最佳选址点,使用遗传算法求解多个充电站多型号电动货车的车辆路径问题。应用实例表明,该方法可以有效地解决新能源货车充电站在农村地区的选址问题。展开更多
针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入...针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入者位置更新不足的问题,设计了一种跳跃跟踪优化策略,通过考虑偏好阻尼因子的跳跃策略设计大步长更新发现者,增加麻雀搜索算法的全局勘探能力和寻优速度,加入者设计动态小步长跟踪领头雀更新位置,同时,利用自适应种群划分机制更新发现者和加入者的比重,增加算法的后期局部开发能力和寻优速度;其次,设计基于扰动因子的Tent映射,在此基础上增加3个参数,使映射分布范围增大,并避免了陷入小周期点和不稳周期点;最后,引入轮廓系数作为评价函数,跳跃跟踪麻雀搜索算法自动寻找较优的p和λ,代替手动输入参数,并融合基于扰动因子的Tent映射优化近邻传播算法,交叉迭代确定最优簇数.使用多种算法聚类University of California Irvine数据集的10种公共数据集,仿真结果表明,本文提出的聚类算法与经典近邻传播算法、基于差分改进的仿射传播聚类算法、基于麻雀搜索算法优化的近邻传播聚类算法和进化近邻传播算法相比具有更优的搜索效率以及聚类精度.对国家信息数据进行了聚类分析,提出的方法更加准确有效合理,具有较好的应用价值.展开更多
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system....Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.展开更多
基金This work was supported by the National Natural Science Foundation of China(71771034,71901011,71971039)the Scientific and Technological Innovation Foundation of Dalian(2018J11CY009).
文摘Affinity propagation(AP)is a classic clustering algorithm.To improve the classical AP algorithms,we propose a clustering algorithm namely,adaptive spectral affinity propagation(AdaSAP).In particular,we discuss why AP is not suitable for non-spherical clusters and present a unifying view of nine famous arbitrary-shaped clustering algorithms.We propose a strategy of extending AP in non-spherical clustering by constructing category similarity of objects.Leveraging the monotonicity that the clusters’number increases with the self-similarity in AP,we propose a model selection procedure that can determine the number of clusters adaptively.For the parameters introduced by extending AP in non-spherical clustering,we provide a grid-evolving strategy to optimize them automatically.The effectiveness of AdaSAP is evaluated by experiments on both synthetic datasets and real-world clustering tasks.Experimental results validate that the superiority of AdaSAP over benchmark algorithms like the classical AP and spectral clustering algorithms.
基金the Science and Technology Research Program of Zhejiang Province,China(No.2011C21036)Projects in Science and Technology of Ningbo Municipal,China(No.2012B82003)+1 种基金Shanghai Natural Science Foundation,China(No.10ZR1400100)the National Undergraduate Training Programs for Innovation and Entrepreneurship,China(No.201410876011)
文摘A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering( APC) process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination( SAP-LC algorithm) have been greatly improved.
文摘为贯彻落实新发展理念,助力实现双碳目标,文章对农村地区电动货车充电站选址进行了研究。运用以相似度矩阵为基础,依靠消息传递迭代更新的近邻传播(affinity propagation,AP)聚类算法,从现有乡镇货运站中筛选出电动货车充电站候选点。在多个属性权重信息未知的情况下,采用基于离差最大化的区间直觉模糊优劣解距离法(technique for order preference by similarity to an ideal solution,TOPSIS)得到最佳选址点,使用遗传算法求解多个充电站多型号电动货车的车辆路径问题。应用实例表明,该方法可以有效地解决新能源货车充电站在农村地区的选址问题。
文摘针对传统近邻传播聚类算法以数据点对之间的相似度作为输入度量,由于需要预设偏向参数p和阻尼系数λ,算法精度无法精确控制的问题,提出了一种跳跃跟踪麻雀搜索算法优化的交叉迭代近邻传播聚类方法.首先,针对麻雀搜索算法中发现者和加入者位置更新不足的问题,设计了一种跳跃跟踪优化策略,通过考虑偏好阻尼因子的跳跃策略设计大步长更新发现者,增加麻雀搜索算法的全局勘探能力和寻优速度,加入者设计动态小步长跟踪领头雀更新位置,同时,利用自适应种群划分机制更新发现者和加入者的比重,增加算法的后期局部开发能力和寻优速度;其次,设计基于扰动因子的Tent映射,在此基础上增加3个参数,使映射分布范围增大,并避免了陷入小周期点和不稳周期点;最后,引入轮廓系数作为评价函数,跳跃跟踪麻雀搜索算法自动寻找较优的p和λ,代替手动输入参数,并融合基于扰动因子的Tent映射优化近邻传播算法,交叉迭代确定最优簇数.使用多种算法聚类University of California Irvine数据集的10种公共数据集,仿真结果表明,本文提出的聚类算法与经典近邻传播算法、基于差分改进的仿射传播聚类算法、基于麻雀搜索算法优化的近邻传播聚类算法和进化近邻传播算法相比具有更优的搜索效率以及聚类精度.对国家信息数据进行了聚类分析,提出的方法更加准确有效合理,具有较好的应用价值.
基金Supported by the National Natural Science Foundation of China(11078001)
文摘Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.
