Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

Affinity propagation(AP)is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

基于Affinity Propagation算法的半监督微博水军识别

对微博网络空间中水军账户的识别研究,有助于清朗网络空间和维护社会安定。首先,文章针对微博水军不断进化、传统特征集无法覆盖现有水军特征,结合水军定义与原始特征,构造了新特征。然后,针对水军账户标注困难,无标注数据又没能充分利用的问题,提出了一种基于Affinity Propagation算法的半监督微博水军识别方法(APDHW)。该方法通过在Affinity Propagation算法中引入欧氏距离Radius阈值,再结合支持向量机分类算法,实现对微博水军识别。通过多组实验对比及实证研究,结果表明文章所提的微博水军识别方法在牺牲少量算法时间的情况下得到较好的识别效果,提升了水军识别的准确率和召回率。

基于Affinity Propagation聚类方法的图像检索技术在数字图书馆中的应用

随着数字图书馆包含的内容逐渐丰富,数字图像也越来越多。为了有效地检索这些图像,迫切需要一种效率更高的检索方法。目前的基于内容的图像检索算法在检索时间和效率上都还不能满足这一需求。本文采用最新提出的Af-finity Propagation聚类方法和颜色-形状直方图特征,提出一种新的检索方法应用到数字图书馆进行图像检索。经过试验证明在查准率、查全率和检索时间上均有较大的提高。

Local and global approaches of affinity propagation clustering for large scale data

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

介绍是一个多重模型基于亲密关系，繁殖(AP ) ， Gaussian 过程(GP ) 和贝叶斯的委员会用机器制造的软察觉到方法(BCM ) 。聚类算术的 AP 被用来根据他们的操作的点聚类训练样品。然后，亚模型被 Gaussian 过程回归(GPR ) 估计。最后，以便得到全球概率的预言，贝叶斯的委员会机器被用来联合亚评估者的产量。建议方法被使用了在氢化裂解器分馏器预言轻石油结束点。实际应用显示它为在化学过程监视的质量的联机预言是有用的。

Adaptive spectral affinity propagation clustering

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.

基于SIFT和Affinity Propagation的遥感图像配准算法

随着传感器和光学影像测量等各种技术的快速发展,航空遥感技术已经在电力巡检、森林防火、地理测绘等领域中发挥着越来越重要的作用,而图像配准作为遥感图像的预处理步骤为图像融合等后续处理提供了参考和依据,目前已经成为遥感图像处理领域的研究热点。文中提出了一种基于尺度不变特征变换(Scale-invariant Feature Transform,SIFT)和仿射传播聚类(Affinity Propagation,AP)的图像配准算法。该算法与原有算法相比优势在于无需预先设定参数,并且实验仿真表明该算法能有效地对多源图像进行高精度的配准,与随机抽样一致算法(Random Sample Consensus,RANSAC)相比提高了正确匹配点的数目。

Improved Semi-supervised Clustering Algorithm Based on Affinity Propagation

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.

Semi-supervised Affinity Propagation Clustering Based on Subtractive Clustering for Large-Scale Data Sets

In the face of a growing number of large-scale data sets, affinity propagation clustering algorithm to calculate the process required to build the similarity matrix, will bring huge storage and computation. Therefore, this paper proposes an improved affinity propagation clustering algorithm. First, add the subtraction clustering, using the density value of the data points to obtain the point of initial clusters. Then, calculate the similarity distance between the initial cluster points, and reference the idea of semi-supervised clustering, adding pairs restriction information, structure sparse similarity matrix. Finally, the cluster representative points conduct AP clustering until a suitable cluster division.Experimental results show that the algorithm allows the calculation is greatly reduced, the similarity matrix storage capacity is also reduced, and better than the original algorithm on the clustering effect and processing speed.

基于改进的Affnity Propagation聚类的木材缺陷识别

本文提出了一种基于快速Affnity Propagation聚类算法的木材缺陷识别方法.通过提取木材图像的颜色矩特征,建立样本特征集X,以平均平方残基为阈值降低样本特征集X及距离矩阵S的维数,自动识别木材缺陷位置并标记.实验表明,该方法的识别速度较传统的AP算法有明显提高,平均识别时间约为0.557s,平均识别查准率约为70.5%,平均识别查全率约为95.6%.

跳跃跟踪SSA交叉迭代AP聚类算法

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.

寒区电动公交充电站选址及定容规划研究

寒区低温环境导致电动公交动力电池容量衰减,充电设施服务范围及规划数量受到影响,给电动公交充电站选址及定容规划带来挑战。为提高电动公交充电站的低温适应性,提出针对寒区电动公交充电站的选址算法及定容模型。首先,在选址规划中,构建充电站渐进覆盖服务半径,利用改进近邻传播聚类算法确定充电站选址点,基于算法聚类中心构建充电站Voronoi图划分充电集群。其次,在定容规划中,构建动力电池低温容量衰减模型,确定寒区电动公交的充电需求;基于容量有限的截尾排队论模型建立充电站有效服务强度、拒绝服务率及充电满意度等约束;引入成本权衡系数,以规划年限内全社会成本最小为优化目标,建立寒区充电站定容规划模型,并设计遗传算法进行求解。最后,以哈尔滨市市区电动公交充电站选址定容规划为例进行分析,算例结果得到9个充电站选址点及其充电集群,以及各充电站的充电机配置数量和各项成本。针对环境温度和成本权衡系数进行灵敏度分析,结果表明:寒区低温环境对充电站的充电机配置数量和各项成本有显著影响,合理权衡充电站和电动公交两者利益有助于提高充电服务满意度,降低全社会成本。

基于简化HMM和时间分段的非侵入式负荷分解算法

针对现有非侵入式负荷分解算法需要以过去时刻的分解结果为依据,从而造成误差累积的问题,提出一种基于简化的隐马尔可夫模型和时间分段的非侵入式负荷分解算法,以实现居民家庭的负荷分解。对负荷的低频功率信号进行分层抽样和聚类分析,构建负荷功率模板并利用独热码对超状态进行编码表示。基于简化的隐马尔可夫模型和普遍生活规律对家庭用电时间段进行划分,在每个时间段内单独训练参数。结合总线数据和各时间段参数实现对各时刻负荷功率的独立求解。基于2种国外公开数据集的测试结果验证了所提算法的准确性和实时性。

考虑动态重构和智能软开关接入的配电网源网荷储联合规划

随着新能源大量接入配电网,新能源出力的不确定性和波动性给配电网规划带来了巨大挑战。在配电网规划中综合考虑源网荷储,可减少新能源不确定性和波动性对规划结果的影响。提出了一种考虑动态重构和智能软开关接入的配电网源网荷储联合规划方法。首先,根据密度峰值聚类的思想提出了基于密度峰值改进的近邻传播聚类算法,对风光荷联合场景进行聚类获得典型日曲线。然后,以规划总费用最小为目标函数,建立了考虑动态重构和智能软开关接入的配电网源网荷储联合规划模型,并基于二阶锥理论,将原非凸非线性规划模型转化为混合整数二阶锥规划模型。最后,在Portugal 54算例上进行仿真验证,证明了所提模型和方法的有效性。

基于边缘计算的台区短期负荷预测方法

配变台区是配电物联网与用户交互的重要纽带,台区短期负荷预测对实现配电物联网的精益化管理具有重要意义。为缓解全部台区上传负荷数据所带来的通信压力,提出一种基于边缘计算的台区短期负荷预测方法,将台区智能配变终端存储30天的历史负荷数据作为样本数据,通过核平滑法对样本数据进行清洗,因样本数据较少考虑将样本归一化后,拆分为标幺曲线与基值分别计算提高预测结果精度。然后通过相关系数法构建历史负荷数据的相关系数矩阵,用相关系数矩阵替换仿射传播相似度矩阵后聚类求得相似日的标幺曲线,再通过加权求和求得待测日的标幺曲线。同时,按照相似日原理预测待测日基值,最终通过待测日标幺曲线和基值反归一化后得到待测日负荷曲线完成预测工作。通过山东某配变30天的历史负荷数据计算验证,表明所提方法可以实现台区负荷量级小、样本少、波动大情形下的合理预测,占用主站计算资源较少,对配网精益化运维具有积极意义。

基于邻近传播聚类算法的LSTM短期风功率预测

考虑气象数据与风电场的历史风功率数据特征,提出一种基于邻近传播聚类算法的长短期记忆神经网络风功率预测模型。利用邻近传播聚类算法(Affinity Propagation,AP)对NWP数据与风功率数据聚类,分别得到多个子集;同时,为了获得能更好反映风功率的特征,采用主成分分析法(Principal Components Analysis,PCA)对NWP子集数据降维处理;最后利用特征匹配方法分别建立基于长短期记忆神经网络预测模型。根据预测日的NWP数据与前一日功率数据选取最优匹配模型进行风功率预测。使用宁夏某风电厂数据进行仿真验证,实验表明所提出方法可以提高短期风功率的预测精度。

采用多任务特征融合的脑电情绪识别方法

特征选择与融合是提升脑电信号情绪解码精度的重要手段之一。然而,当前脑电情绪解码中的特征选择方法常忽略了脑电信号内在数据结构的隐含信息。该文提出一种基于近邻传播聚类的多任务特征融合方法,通过L_(2,1)范数约束实现稀疏特征选择,同时利用图拉普拉斯正则化保持不同子类间的潜在关系。该算法在不揭示真实样本标签的情况下,在子任务空间有效融合脑网络空间拓扑结构信息和微分熵信息,为高精度脑电信号情绪解码提供具有更高情绪表征能力的特征。DEAP和SEED数据集以及本实验室数据集的分析结果表明,该文提出的方法能显著提高脑电情绪解码的精度。

融合AP聚类算法和宽度学习系统的分布外硬盘故障预测

硬盘是云数据中心最主要的存储设备,硬盘故障预测是保障数据安全的重要手段。但是,硬盘的故障与健康样本之间存在着极端的数量不平衡问题,这会导致模型偏差;此外,不同型号的硬盘数据分布存在一定的差异,在特定硬盘数据上训练的模型往往不适用于其他硬盘。对于这两个问题,文中提出了一种融合AP聚类算法和宽度学习系统的分布外硬盘故障预测方法。针对样本不平衡问题,文中使用AP聚类算法对硬盘故障出现前一阶段的样本集进行聚类,将与故障样本处于同一聚类簇的样本扩充为故障样本。针对不同型号硬盘分布存在差异的问题,文中结合流形正则化框架和宽度学习系统来学习硬盘数据的低维结构,提高模型对未知分布数据的泛化能力。实验结果表明,在AP聚类算法重采样的样本集上,相较于用于对比的重采样方法得到的样本集,多种故障预测方法的F1_Score取得了平均0.2的提升。此外,在分布外硬盘故障预测任务上,所提模型的F1_Score相比对比方法提升了0.1~0.2。

考虑节点功率储备与GIN中心性的主动配电网动态集群电压控制

为应对大规模分布式光伏(photovoltaic,PV)接入引起的主动配电网电压越限问题,降低控制策略的时序复杂性,提出一种考虑节点功率储备与节点影响力(global importance of each node,GIN)的主动配电网动态集群电压控制方法。首先,通过考虑系统各节点的功率储备度,定义聚类算法的电压灵敏度-功率储备度(voltage sensitivity-power reserve,VS-PR)综合电气距离量度。进而,以GIN算法改进亲和力传播(affinity propagation,AP)聚类算法,实现网络集群划分与主导节点选取。然后,建立主动配电网集群电压控制模型,并通过动态粒子群算法(dynamic particle swarm optimization,D-PSO)进行模型求解。最后,通过建立基于MATLAB 2021b平台的IEEE 33节点仿真算例对比分析,验证了所提动态集群划分与电压控制方法的正确性和有效性。