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.

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.

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.

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.

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。

Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.

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%.

Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm

A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.

Steganalysis Using Fractal Block Codes and AP Clustering in Grayscale Images

This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.

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。

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。

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。

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。

一种利用熵函数和Affinity Propagation聚类的超图模型优化方法

属性图相似性阈值对类属超图(CSHG)模型的训练结果具有重要影响。在满足聚类准确性的条件下,利用定义的熵函数给出优化CSHG模型结构的相似性阈值,并得到初始优化的CSHG模型,进一步利用FTOG之间的相似性矩阵得到最简CSHG模型结构。另外,利用亲缘传播聚类(affinity propagation clustering)方法去除FTOG聚类中的冗余属性图,最终得到最优的CSHG模型。实验结果表明,本方法是有效的。

基于近邻传播聚类-K均值聚类的工业用户用电模式挖掘方法

为了充分发挥用户负荷的可调节潜力,提出了一种基于近邻传播聚类-K均值聚类的工业用户用电模式挖掘方法。首先,比较K均值聚类和近邻传播聚类-K均值聚类的优缺点。在工业用户的选取上,选择最佳聚类数均为3的工业用户负荷数据作为被分析对象以便聚类,借助MATLAB工具对用户负荷数据进行聚类,得到了3组所需的聚类中心,再绘制成曲线以便观察和后续提取特征指标。