Journal ArticleDOI
Semi-Supervised Clustering Based on Affinity Propagation Algorithm: Semi-Supervised Clustering Based on Affinity Propagation Algorithm
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TLDR
Experimental results show that this semi-supervised clustering method based on affinity propagation reaches its goal for complex datasets, and this method outperforms the comparative methods when there are a large number of pairwise constraints.About:
This article is published in Journal of Software.The article was published on 2009-04-07. It has received 61 citations till now. The article focuses on the topics: Affinity propagation & Correlation clustering.read more
Citations
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Journal ArticleDOI
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors
TL;DR: The experimental results demonstrate that the proposed clustering algorithm can find cluster centers, recognize clusters regardless of their shape and dimension of the space in which they are embedded, be unaffected by outliers, and can often outperform DPC, AP, DBSCAN and K-means.
Journal ArticleDOI
A multiobjective evolutionary algorithm to find community structures based on affinity propagation
TL;DR: A Multiobjective Evolutionary Algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection and has faster convergence speed compared with seven other state-of-art algorithms.
Journal ArticleDOI
Research of semi-supervised spectral clustering algorithm based on pairwise constraints
TL;DR: This paper proposes an effective clustering algorithm, called a semi-supervised spectral clustering algorithms based on pairwise constraints, in which the similarity matrix of data points is adjusted and optimized by Pairwise constraints.
Journal ArticleDOI
Research Progress on Semi-Supervised Clustering
Yue Qin,Yue Qin,Shifei Ding,Shifei Ding,Lijuan Wang,Lijuan Wang,Lijuan Wang,Yanru Wang,Yanru Wang +8 more
TL;DR: The introduction of several semi-supervised clustering methods in this paper can show the advantages of semi- supervisory clustering over traditional clustering, and the related literature in recent years is summarized.
Journal ArticleDOI
Unsupervised Hyperspectral Remote Sensing Image Clustering Based on Adaptive Density
Huan Xie,Ang Zhao,Shengyu Huang,Jie Han,Sicong Liu,Xiong Xu,Xin Luo,Haiyan Pan,Qian Du,Xiaohua Tong +9 more
TL;DR: This work proposes an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels, and presents an adaptive-bandwidth probability density function, which determines the bandwidth on the basis of the Gaussian assumption.
References
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Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Clustering by Passing Messages Between Data Points
Brendan J. Frey,Delbert Dueck +1 more
TL;DR: A method called “affinity propagation,” which takes as input measures of similarity between pairs of data points, which found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.
Proceedings Article
Distance Metric Learning with Application to Clustering with Side-Information
TL;DR: This paper presents an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in �”n, learns a distance metric over ℝn that respects these relationships.
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Constrained K-means Clustering with Background Knowledge
TL;DR: This paper demonstrates how the popular k-means clustering algorithm can be protably modied to make use of information about the problem domain that is available in addition to the data instances themselves.
Proceedings Article