Fast affinity propagation clustering based on incomplete similarity matrix

2016 ◽  
Vol 51 (3) ◽  
pp. 941-963 ◽  
Author(s):  
Leilei Sun ◽  
Chonghui Guo ◽  
Chuanren Liu ◽  
Hui Xiong
Keyword(s):  
Affinity Propagation ◽  
Similarity Matrix ◽  
Affinity Propagation Clustering ◽  
Incomplete Similarity

scholarly journals A Full Coverage Reconstruction Algorithm for the Electromagnetic Map Based on Affinity Propagation Clustering

2021 ◽  
Author(s):  
Dongming Lin ◽  
Hongjun Wang
Keyword(s):  
Reconstruction Algorithm ◽  
Affinity Propagation ◽  
Target Area ◽  
Electromagnetic Propagation ◽  
Electromagnetic Data ◽  
Affinity Propagation Clustering ◽  
Domain Mapping ◽  
Total Data ◽  
Sample Points ◽  
Map Data

Abstract Considering the reconstruction of electromagnetic maps without the prior information of electromagnetic propagation environment in the target area, a new algorithm based on affinity propagation clustering is proposed to complete the electromagnetic map reconstruction of the target area from points to surfaces and then from points and surfaces to a larger surface. Firstly, according to the actual situation, the target area is reasonably divided into grids. Electromagnetic data is sampled by distributed sensing nodes, and a certain number of sample points are selected for affinity propagation clustering to determine the locations of centers of sample points. Secondly, for the incomplete sample data, the Kriging algorithm is used to reconstruct the small circular electromagnetic maps. The class center is the center of the circle and the radius is certain. After that, the obtained small area electromagnetic map data and the data obtained from the sample points are used for domain mapping processing, and the electromagnetic data of a larger area of the target area is obtained. Finally, the overall electromagnetic map is reconstructed through data fusion. The simulation results show that the proposed algorithm is better than several interpolation algorithms. When sample points account for 0.1 of total data points, the RMSE of the result is less than 1.5.

Summary of Affinity Propagation

2011 ◽  
Vol 268-270 ◽  
pp. 811-816
Author(s):  
Yong Zhou ◽  
Yan Xing
Keyword(s):  
Clustering Algorithm ◽  
Large Data ◽  
Large Data Sets ◽  
Affinity Propagation ◽  
Damping Factor ◽  
Data Sets ◽  
Similarity Matrix ◽  
Data Points

Affinity Propagation(AP)is a new clustering algorithm, which is based on the similarity matrix between pairs of data points and messages are exchanged between data points until clustering result emerges. It is efficient and fast , and it can solve the clustering on large data sets. But the traditional Affinity Propagation has many limitations, this paper introduces the Affinity Propagation, and analyzes in depth the advantages and limitations of it, focuses on the improvements of the algorithm — improve the similarity matrix, adjust the preference and the damping-factor, combine with other algorithms. Finally, discusses the development of Affinity Propagation.

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