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.

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.

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。

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

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

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.

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.

Analyzing potential tourist behavior using PCA and modified affinity propagation clustering based on Baidu index:taking Beijing city as an example

In recent years,when planning and determining a travel destination,residents often make the best of Internet techniques to access extensive travel information.Search engines undeniably reveal visitors'real-time preferences when planning to visit a destination.More and more researchers have adopted tourism-related search engine data in the field of tourism prediction.However,few studies use search engine data to conduct cluster analysis to identify residents'choice toward a tourism destination.In the present study,146 keywords related to“Beijing tourism”are obtained from Baidu index and principal component analysis(PCA)is applied to reduce the dimensionality of keywords obtained by Baidu index.Modified affinity propagation(MAP)clustering algorithm is used to classify provinces into several groups to identify the choice of residents to travel to Beijing.The result shows that residents in Hebei province are most likely to travel to Beijing.The cluster result also shows that PCA–MAP performs better than other clustering methods such as K-means,linkage,and Affinity Propogation(AP)in terms of silhouette coefficient and Calinski–Harabaz index.We also distinguish the difference of residents’choice to travel to Beijing during the peak tourist season and off-season.The residents of Tianjing are inclined to travel to Beijing during the peak tourist season.The residents of Guangdong,Hebei,Henan,Jiangsu,Liaoning,Shanghai,Shandong,and Zhejiang have high attention to travel to Beijing during both seasons.

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.

基于BERT模型的“互联网+政务”群众留言文本热点追踪研究

[研究目的]大数据时代,提升政府治理能力和人民群众生活幸福感是各级政府核心任务,尤其是从群众留言文本中获取民众关注的热点问题,从而快速响应并及时解决群众反馈的问题,从群众留言文本中提取有价值的热点对政府部门显得尤其紧迫和必要。[研究方法]以从上海市政府信箱、上海市委信箱、上海信访的“互联网+政务”网络平台爬取的群众留言29074条数据为研究对象,对比分析BERT、LSTM以及FastText三个模型的分类效果,选择BERT模型构建群众留言热点追踪框架,对反馈最多的住房规划、医疗卫生和交通出行三类问题使用Affinity Propagation聚类算法与BERT实体命名识别相结合的方式对每个分类下的问题进行聚类,最后通过聚类形成的问题进行热度计算得出群众反馈的热点问题。[研究结论]从热点追踪的结果可知住房安全、疫情防控和两港快线设立分别为住房规划、医疗卫生和交通出行三类留言中讨论热度最高的问题,据此提出针对性建议。

含混合储能的交直流配电网日经济优化运行

针对高渗透可再生能源接入的交直流混合配电网经济性和灵活调节性不足的问题,提出一种配合降压变压器(step down transformer,SDT)和电压源型变换器(voltage source converter,VSC)调压策略的含混合储能系统(hybrid energy storage system,HESS)交直流配电网日级别经济运行优化方法。首先,基于有功/无功-电压综合灵敏度对配电网进行分区,确定HESS的接入容量与位置;其次,基于希尔伯特-黄变换(Hilbert-Huang transform,HHT)原理对由锂电池和超级电容构成的HESS进行功率分配;然后,建立了计及HESS全生命周期的运行成本和主网购电成本的交直流混流配电网日级别经济运行优化模型;最后,对该典型二阶锥规划问题进行求解。改进IEEE33节点交直流混合配电网仿真实验表明:在合理选址定容基础上,HESS在平抑系统高频功率信号及经济性上优势明显;HESS联合SDT及VSC电压控制,可以有效降低HESS运行中出现的电压偏离程度,减小了电压约束对HESS充放电过程的影响,并进一步提升了含储能配电网经济运行能力及电压稳定性。

基于个性化场景的5G基站节能方法

为了最大化基站的可节能空间,解决全网绿色基站的智能发现问题,通过改进Affinity Propagation聚类算法对基站日负荷曲线进行自适应聚类,并进一步挖掘分析周效应下的日潮汐现象和汐节能时段,智能化识别基站的个性化节能场景,从而周期性地采取差异化节能策略。实验分析验证了该算法的高效性和准确性,预计节能空间可达20%以上,可应用于5G基站能耗的智能化管理,提高5G网络能效。

Multi Boost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

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.

Track Association for Dynamic Target Tracking System Based on AP Algorithm

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.

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.

TEXTINSIGHT: A NEW TEXT VISUALIZATION SYSTEM BASED ON ENTROPY AND GMAP

In recent years, text visualization has been widely acknowledged as an effective approach for understanding the structure and patterns hidden in complicated textual information. In this paper, we propose a new visualization system called TextInsight with two of our contributions. Firstly, a textual entropy theory is introduced to encode the semantic importance distribution in the corpus. Based on the proposed multidimensional joint probability histogram in vector fields, the improved algorithm provides a novel way to position valuable information in massive short texts accurately. Secondly, a map-like metaphor is generated to visualize the textual topics and their relationships. For the problem of over-segmentation in the layout and clustering procedure, we propose an optimization algorithm combining Affinity Propagation(AP) and MultiDimensional Scaling(MDS), and the improved geographical representation is more comprehensible and aesthetically appealing. Our experimental results and initial user feedback suggest that this system is effective in aiding text analysis.

Uncovering current pyroregions in Italy using wildfire metrics

Background:Pyrogeography is a major field of investigation in wildfire science because of its capacity to describe the spatial and temporal variations of fire disturbance.We propose a systematic pyrogeographic analytical approach to cluster regions on the basis of their pyrosimilarities.We employed the Affinity Propagation algorithm to cluster pyroregions using Italian landscape as a test bed and its current wildfire metrics in terms of density,seasonality and stand replacing fire ratio.A discussion follows on how pyrogeography varies according to differences in the human,biophysical,socioeconomic,and climatic spheres.Results:The algorithm identified seven different pyroregion clusters.Two main gradients were identified that partly explain the variability of wildfire metrics observed in the current pyroregions.First,a gradient characterized by increasing temperatures and exposure to droughts,which coincides with a decreasing latitude,and second,a human pressure gradient displaying increasing population density in areas at lower elevation.These drivers exerted a major influence on wildfire density,burnt area over available fuels and stand replacing,which were associated to warmdry climate and high human pressure.The study statistically highlighted the importance of a North–South gradient,which represents one of the most important drivers of wildfire regimes resulting from the variations in climatic conditions but showing collinearity with socioeconomic aspects as well.Conclusion:Our fully replicable analytical approach can be applied at multiple scales and used for the entire European continent to uncover new and larger pyroregions.This could create a basis for the European Commission to promote innovative and collaborative funding programs between regions that demonstrate pyrosimilarities.

Online detection of bursty events and their evolution in news streams

Online monitoring of temporally-sequenced news streams for interesting patterns and trends has gained popularity in the last decade.In this paper,we study a particular news stream monitoring task:timely detection of bursty events which have happened recently and discovery of their evolutionary patterns along the timeline.Here,a news stream is represented as feature streams of tens of thousands of features(i.e.,keyword.Each news story consists of a set of keywords.).A bursty event therefore is composed of a group of bursty features,which show bursty rises in frequency as the related event emerges.In this paper,we give a formal definition to the above problem and present a solution with the following steps:(1) applying an online multi-resolution burst detection method to identify bursty features with different bursty durations within a recent time period;(2) clustering bursty features to form bursty events and associating each event with a power value which reflects its bursty level;(3) applying an information retrieval method based on cosine similarity to discover the event's evolution(i.e.,highly related bursty events in history) along the timeline.We extensively evaluate the proposed methods on the Reuters Corpus Volume 1.Experimental results show that our methods can detect bursty events in a timely way and effectively discover their evolution.The power values used in our model not only measure event's bursty level or relative importance well at a certain time point but also show relative strengths of events along the same evolution.