A new approach data processing: density-based spatial clustering of applications with noise (DBSCAN) clustering using game-theory

Due to the unpredictable growth of data in various fields, rapid clustering of big data is seriously needed in order to identify the hidden structure of data and discover the relationships between objects. Among clustering methods, density-based clustering methods have an acceptable processing speed...

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Published in:Soft computing (Berlin, Germany) Vol. 29; no. 3; pp. 1331 - 1346
Main Authors: Kazemi, Uranus, Soleimani, Seyfollah
Format: Journal Article
Language:English
Published: Heidelberg Springer Nature B.V 01.02.2025
Subjects:
Accuracy
Algorithms
Big Data
Clustering
Data compression
Data mining
Data points
Data processing
Datasets
Density
Efficiency
Fluid dynamics
Game theory
Massive data points
Methods
Optimization
Parameter sensitivity
Spatial data
Synthetic data
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Abstract Due to the unpredictable growth of data in various fields, rapid clustering of big data is seriously needed in order to identify the hidden structure of data and discover the relationships between objects. Among clustering methods, density-based clustering methods have an acceptable processing speed for dealing with big data with high dimensions. However, some methods have fixed parameters that are certainly not optimized for all sections. In addition, the complexity of these clustering methods strongly depends on the number of objects. In this paper, a clustering method is presented in order to increase clustering performance and parameter sensitivity according to game-theory and using the concept of Nash equilibrium and dense games, the optimal parameter for clustering is selected and between noise and points clusters make a difference. This method includes (1) searching the grid with several spaces in which there is no cluster, (2) identifying the player through high density data points in order to determine the parameters and (3) combining the clusters to make the game and (4) merging the nearby clusters. The performance of the proposed method was evaluated in four big synthetic datasets, eight real datasets labeled and unlabeled. The obtained results indicate the superiority of the proposed method over SOM, K-means, DBSCAN, SCGPSC methods in terms of accuracy and purity in processing time.
AbstractList Due to the unpredictable growth of data in various fields, rapid clustering of big data is seriously needed in order to identify the hidden structure of data and discover the relationships between objects. Among clustering methods, density-based clustering methods have an acceptable processing speed for dealing with big data with high dimensions. However, some methods have fixed parameters that are certainly not optimized for all sections. In addition, the complexity of these clustering methods strongly depends on the number of objects. In this paper, a clustering method is presented in order to increase clustering performance and parameter sensitivity according to game-theory and using the concept of Nash equilibrium and dense games, the optimal parameter for clustering is selected and between noise and points clusters make a difference. This method includes (1) searching the grid with several spaces in which there is no cluster, (2) identifying the player through high density data points in order to determine the parameters and (3) combining the clusters to make the game and (4) merging the nearby clusters. The performance of the proposed method was evaluated in four big synthetic datasets, eight real datasets labeled and unlabeled. The obtained results indicate the superiority of the proposed method over SOM, K-means, DBSCAN, SCGPSC methods in terms of accuracy and purity in processing time.
Author Kazemi, Uranus
Soleimani, Seyfollah
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CitedBy_id crossref_primary_10_1007_s11227_025_07502_5
crossref_primary_10_1016_j_atech_2025_101277
crossref_primary_10_1007_s11227_025_07329_0
crossref_primary_10_1038_s41598_025_07404_9
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Snippet Due to the unpredictable growth of data in various fields, rapid clustering of big data is seriously needed in order to identify the hidden structure of data...
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SubjectTerms Accuracy
Algorithms
Big Data
Clustering
Data compression
Data mining
Data points
Data processing
Datasets
Density
Efficiency
Fluid dynamics
Game theory
Massive data points
Methods
Optimization
Parameter sensitivity
Spatial data
Synthetic data
Title A new approach data processing: density-based spatial clustering of applications with noise (DBSCAN) clustering using game-theory
URI https://www.proquest.com/docview/3172015983
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