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An improved density peaks clustering based on sparrow search algorithm
Density peaks clustering (DPC) algorithm has attracted the attention of scholars because of its simplicity and efficiency. However, it certainly has...
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Density peaks clustering algorithm with connected local density and punished relative distance
Density peaks clustering (DPC) algorithm has been widely applied in many fields due to its innovation and efficiency. However, the original DPC...
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OALDPC: oversampling approach based on local density peaks clustering for imbalanced classification
SMOTE has been favored by researchers in improving imbalanced classification. Nevertheless, imbalances within minority classes and noise generation...
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An improved density peaks clustering algorithm using similarity assignment strategy with K-nearest neighbors
Some particular shaped datasets, such as manifold datasets, have restrictions on density peak clustering (DPC) performance. The main reason of...
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Density peaks algorithm based on information entropy and merging strategy for power load curve clustering
To solve the problems of density peaks clustering (DPC) algorithm sensitive to cutoff distance and subjectivity of clustering center selection, we...
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A spectral clustering algorithm based on attribute fluctuation and density peaks clustering algorithm
Spectral clustering (SC) has become a popular choice for data clustering by converting a dataset to a graph structure and then by identifying optimal...
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ND-S: an oversampling algorithm based on natural neighbor and density peaks clustering
There are a large number of imbalanced classification problems in the real world. Due to the imbalance in the amount of data and the complex nature...
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Grid-DPC: Improved density peaks clustering based on spatial grid walk
Traditional clustering methods need to find the initial centers first. A reasonable cluster center can improve the efficiency and accuracy of the...
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Fast density peaks clustering algorithm in polar coordinate system
Density peaks clustering (DPC) algorithm provides an efficient method to quickly find cluster centers with decision graphs. In recent years, due to...
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An efficient clustering algorithm based on searching popularity peaks
In order to address some deficiencies of the density peak clustering algorithm, namely sensitivity to density kernels and challenges with large...
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Density Ratio Peak Clustering
Clustering is an important means of obtaining hidden information, and is widely used in economics, biomedicine and other disciplines. Data imbalance...
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Cost-Effective Clustering by Aggregating Local Density Peaks
Hierarchical clustering algorithms that provide tree-shaped results can be regarded as data summarization and thus play an important role in the...
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Density Peaks Clustering Based on Jaccard Similarity and Label Propagation
Cognitive computing involves discovering hidden rules and patterns in massive volumes of data. Density peaks clustering (DPC) is a powerful data...
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Multi-exemplar affinity propagation clustering based on local density peak
As the representatives of subclusters in multi-exemplar affinity propagation clustering (MEAP), exemplars are important in generating and merging...
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Ultra-DPC: Ultra-scalable and Index-Free Density Peak Clustering
Density-based clustering is a fundamental and effective tool for recognizing connectivity structure. The density peak, the data object with the...
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Density peak clustering using tensor network
We introduce a density-based clustering algorithm with tensor networks. In order to demonstrate its effectiveness, we apply it to various types of...
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Expanded relative density peak clustering for image segmentation
The density peak clustering (DPC) is one of the most popular algorithms for segmenting images due to its simplicity and efficiency. Since DPC and its...
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Clustering Brain Connectomes Through a Density-Peak Approach
The density-peak (DP) algorithm is a mode-based clustering method that identifies cluster centers as data points being surrounded by neighbors with...
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A dynamic density-based clustering method based on K-nearest neighbor
Many density-based clustering algorithms already proposed in the literature are capable of finding clusters with different shapes, sizes, and...
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An image segmentation fusion algorithm based on density peak clustering and Markov random field
Image segmentation is a crucial task in the field of computer vision. Markov random fields (MRF) based image segmentation method can effectively...
