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
为有效实现海量数据的非线性聚类,提出基于GraphLab的分布式流式近邻传播算法——GStrAP(GraphLab based stream affinity propagation)。该算法将数据抽象为有向无环图模型,采用"Gather-Apply-Scatter"的模式完成数据同步和...为有效实现海量数据的非线性聚类,提出基于GraphLab的分布式流式近邻传播算法——GStrAP(GraphLab based stream affinity propagation)。该算法将数据抽象为有向无环图模型,采用"Gather-Apply-Scatter"的模式完成数据同步和算法迭代。在人工合成流形数据3D Clusters、Aggregation、Flame和Pathbased数据集上分别采用不同数据规模以及与传统K-means的聚类性能做对比,实验表明:基于GraphLab的近邻传播算法对数据规模具有良好的拓展性,在保持算法聚类效果的同时,有效降低时间复杂度。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。
