Results 31 to 40 of about 798,948 (275)
Generalizing Local Density for Density-Based Clustering [PDF]
Symmetry, 2021 Discovering densely-populated regions in a dataset of data points is an essential task for density-based clustering. To do so, it is often necessary to calculate each data point’s local density in the dataset. Various definitions for the local density have been proposed in the literature.
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The Communication Relationship Discovery Based on the Spectrum Monitoring Data by Improved DBSCAN
IEEE Access, 2019 The communication relationship can reflect the behavior relationship between different communication targets. The in-depth analysis of the communication relationship can obtain the behaviors of communication individuals, and speculate their hierarchical ...
Changkun Liu +7 more
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Research on Communication Network Structure Mining Based on Spectrum Monitoring Data
IEEE Access, 2020 The physical characteristics of the massive spectrum signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the ...
Changkun Liu +7 more
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Clustering Algorithm Based on Density of Data [PDF]
E3S Web of Conferences, 2021 The k_means clustering algorithm has very extensive application. The paper gives out_in clustering algorithm based on density. The algorithm combines distance with data density to adapt to data distribution.
Ma Yong
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Fast multi-image matching via density-based clustering [PDF]
, 2017 We consider the problem of finding consistent matches across multiple images. Previous state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms.
Daniilidis, K. +3 more
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Journal of Marine Science and Engineering, 2022 Automatic Identification System (AIS) equipment can aid in identifying ships, reducing ship collision risks and ensuring maritime safety. However, the explosion of massive AIS data has caused increasing data processing challenges affecting their ...
Jufu Zhang +3 more
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Superpixel Segmentation Based on Grid Point Density Peak Clustering
Sensors, 2021 Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem.
Xianyi Chen, Xiafu Peng, Sun’an Wang
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Hybrid Structure-Adaptive RBF-ELM Network Classifier
IEEE Access, 2017 In this paper, a hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) network classifier is presented. HSARBF-ELM consists of a structure-adaptive radial basis function (SARBF) network and an extreme learning machine (ELM)
Hui Wen +3 more
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Density Peaks Clustering with Optimized Allocation Strategy
Jisuanji kexue yu tansuo, 2020 Focused on the issue that density peaks clustering algorithm will make mistakes when facing data sets allocation with complex structures, a kind of density peaks clustering with optimized allocation strategy (ODPC) is proposed in this paper. Firstly, the
DING Zhicheng, GE Hongwei
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Superpixel Segmentation Algorithm by Spatial Constrained Density Clustering
Journal of Harbin University of Science and Technology, 2020 The superpixel segmentation is an important pre-processing step in computer image processing. Thetraditional superpixel segmentation algorithm based on density clustering is better for boundary processing，but the resulting superpixel shape is irregular ...
HAN Jian-hui, TANG Jun-chao
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