An Improved Density Peak Clustering Algorithm for Multi-Density Data Article Swipe
Lifeng Yin
,
Yingfeng Wang
,
Huayue Chen
,
Wu Deng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22228814
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22228814
Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples.
Related Topics
Concepts
Cluster analysis
Nearest-neighbor chain algorithm
sort
Data mining
Determining the number of clusters in a data set
Algorithm
k-medians clustering
CURE data clustering algorithm
Computer science
Correlation clustering
Selection (genetic algorithm)
Cluster (spacecraft)
k-nearest neighbors algorithm
Data point
Point (geometry)
Single-linkage clustering
Graph
Mathematics
Canopy clustering algorithm
Artificial intelligence
Theoretical computer science
Geometry
Programming language
Information retrieval
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22228814
- https://www.mdpi.com/1424-8220/22/22/8814/pdf?version=1668498487
- OA Status
- gold
- Cited By
- 7
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309312086
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309312086Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s22228814Digital Object Identifier
- Title
-
An Improved Density Peak Clustering Algorithm for Multi-Density DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-15Full publication date if available
- Authors
-
Lifeng Yin, Yingfeng Wang, Huayue Chen, Wu DengList of authors in order
- Landing page
-
https://doi.org/10.3390/s22228814Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/22/22/8814/pdf?version=1668498487Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/22/22/8814/pdf?version=1668498487Direct OA link when available
- Concepts
-
Cluster analysis, Nearest-neighbor chain algorithm, sort, Data mining, Determining the number of clusters in a data set, Algorithm, k-medians clustering, CURE data clustering algorithm, Computer science, Correlation clustering, Selection (genetic algorithm), Cluster (spacecraft), k-nearest neighbors algorithm, Data point, Point (geometry), Single-linkage clustering, Graph, Mathematics, Canopy clustering algorithm, Artificial intelligence, Theoretical computer science, Geometry, Programming language, Information retrievalTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
- References (count)
-
55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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