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 l...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.展开更多
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 e...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.展开更多
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
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 syst...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。展开更多
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 chal...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.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
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,...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.展开更多
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 visuali...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.展开更多
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 analyti...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.展开更多
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 b...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.展开更多
基金supported by Research Team Development Funds of L.Xue and Z.H.Ouyang,Electronic Countermeasure Institute,National University of Defense Technology。
文摘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.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 51075083)
文摘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.
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。
基金supported by the National Natural Science Foundation of China under Grant No. 61070208the Postdoctor Foundation from North Electronic Systems Engineering Corporation
文摘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.
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.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61101122 and 61071105)
文摘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.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2013AA7013033)
文摘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.
文摘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.
基金Project (No.2008BAH26B00) supported by the National Key Technology R & D Program of China
文摘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.