Results 231 to 240 of about 798,948 (275)

Relative density-based clustering algorithm for identifying diverse density clusters effectively

Neural Computing and Applications, 2021
Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC.
Yuying Wang, Youlong Yang
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Density-Based Clustering for Adaptive Density Variation

2021 IEEE International Conference on Data Mining (ICDM), 2021
Cluster analysis plays a crucial role in data mining and knowledge discovery. Although many researchers have investigated clustering algorithms over the past few decades, most of the well-known algorithms have shortcomings when dealing with clusters of arbitrary shapes and varying sizes and in the presence of noise and outliers.
Qian, Li, Plant, Claudia, Böhm, Christian   +2 more
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Density decay graph-based density peak clustering

Knowledge-Based Systems, 2021
Abstract In 2014, Rodriguez and Laio proposed a famous clustering algorithm based on a fast search and find density peaks dubbed as DPC (Rodriguez and Laio, 2014). DPC has been widely used in many fields because of its simplicity and effectiveness. However, DPC has two obvious drawbacks.
Zhiyong Zhang, Qingsheng Zhu, Fan Zhu, Junnan Li, Dongdong Cheng, Yi Liu, Jiangmei Luo   +6 more
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Density-ratio based clustering for discovering clusters with varying densities

Pattern Recognition, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhu, Ye, Ting, Kai Ming, Carman, Mark J.
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Enhancing Density Peak Clustering via Density Normalization

IEEE Transactions on Industrial Informatics, 2020
Clustering is able to find out implicit data distribution and is especially useful in data driven machine learning. Density based clustering has an attractive property of detecting clusters of arbitrary structures. The density peak algorithm makes use of two assumptions to detect cluster centers and then groups the other data.
Jian Hou, Aihua Zhang
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Attractor Density Clustering

2017
Stationary dynamical systems have invariant measures (or densities) that are characteristic of the particular dynamical system. We develop a method to characterize this density by partitioning the attractor into the smallest regions in phase space that contain information about the structure of the attractor.
T. L. Carroll, J. M. Byers
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Fuzzy Density Peaks Clustering

IEEE Transactions on Fuzzy Systems, 2021
As an exemplar-based clustering method, the well-known density peaks clustering (DPC) heavily depends on the computation of kernel-based density peaks, which incurs two issues: first, whether kernel-based density can facilitate a large variety of data well, including cases where ambiguity and uncertainty of the assignment of the data points to their ...
Zekang Bian, Fu-Lai Chung, Shitong Wang
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Neighborhood density correlation clustering

2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020
In this paper, by analyzing the advantages and disadvantages of existing clustering analysis algorithms, a new neighborhood density correlation clustering (NDCC) algorithm for quickly discovering arbitrary shaped clusters is proposed that avoids the clustering errors caused by iso-density points between clusters.
Zhenggang Wang, Liu Zhong
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Horizontal Federated Density Peaks Clustering

IEEE Transactions on Neural Networks and Learning Systems
Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied and favored by many scholars because of its simplicity, fewer parameters, and no iteration. However, in previous improvements of DPC, the issue of privacy data leakage was not considered, and the "Domino" effect caused by the misallocation of noncenters has not ...
Shifei Ding, Chao Li, Xiao Xu, Lili Guo, Ling Ding, Xindong Wu   +5 more
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Data density based clustering

2014 14th UK Workshop on Computational Intelligence (UKCI), 2014
A new, data density based approach to clustering is presented which automatically determines the number of clusters. By using RDE for each data sample the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use.
Hyde, Richard, Angelov, Plamen
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