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
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 ...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.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
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
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
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,...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.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
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the National Natural Science Foundation of China (Nos. 60533090 and 60603096)the National Hi-Tech Research and Development Program (863) of China (No. 2006AA010107)+2 种基金the Key Technology R&D Program of China (No. 2006BAH02A13-4)the Program for Changjiang Scholars and Innovative Research Team in University of China (No. IRT0652)the Cultivation Fund of the Key Scientific and Technical Innovation Project of MOE, China (No. 706033)
文摘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.
基金This research has been partially supported by the national natural science foundation of China (51175169) and the national science and technology support program (2012BAF02B01).
文摘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.