Application of DBSCAN Algorithm in Data Sampling

Deng, Dingsheng

doi:10.1088/1742-6596/1617/1/012088

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…This algorithm involves two parameters: the first one is 'minpts' (minimum number of points), determining the density required for a region to be considered, and the second one utilizes 'minpts' for clustering. Another is eps (ϵ), this parameter works for a distance measure that is used to locate the points (Deng, 2020).…”

Section: Dbscan (Density-based Spatial Clustering Of Application With...mentioning

confidence: 99%

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Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Islam¹,

Seth²,

Bhadra³

et al. 2024

JDSIS

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In this era, feature clustering is a prominent technique in data mining. Features clustering have also huge application in biomedical research for multiple purpose including grouping, features reduction and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effect and human reaction have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structure in both labeled and unlabeled data sets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we are used five different type of feature selection like Minimum Redundancy - Maximum Relevance (mRMR), Random Forest, Normalized Mutual Information Feature Selection (NMIFS), F-Test and Chi-Square and five different type of Clustering algorithms like Hierarchical Clustering, Density-based spatial clustering of applications with noise (DBSCAN) Clustering, K-Means Clustering, Shrinkage Clustering, and Fuzzy C-Means Clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society.

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Section: Dbscan (Density-based Spatial Clustering Of Application With...mentioning

confidence: 99%

“…The DBSCAN clustering algorithm is useful with unsupervised learning of the data. This clustering algorithm can discover outliers (Deng, 2020). In fuzzy C-means clustering, each cluster is assigned a membership matrix, which determines the extent to which each sample is associated with the cluster (Li et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Islam¹,

Seth²,

Bhadra³

et al. 2024

JDSIS

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show abstract

“…Considering the slow search speed and strong dependence on initial population selection of the genetic algorithm, Sherar and Zulkernine [29] implemented a parallel clustering algorithm with particle swarm optimization in the Apache Spark framework to improve the accuracy of data partitioning. Deng [30] proposed a parallel density clustering algorithm under MapReduce, which partitions data by using the particle swarm optimization algorithm and uses the k-dist graph to fnd local clustering parameters. In addition, Ashish et al [31] proposed a parallel clustering method using the bat algorithm, which achieves fast and efcient work by dividing large data sets into small blocks and then clustering these smaller blocks in parallel.…”

Section: State Of the Artmentioning

confidence: 99%

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

Zhang

2023

Advances in Multimedia

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The density-based applied spatial clustering algorithm is an algorithm based on high-density interconnected regions, which discovers class clusters of arbitrary shapes in noisy data sets and is widely used. However, it suffers from slow computation speed due to large-scale disk I/O and clustering bias due to uneven density class clusters and poor parameter search ability. To address these problems, a parallel density clustering algorithm based on an improved fruit fly optimization algorithm and Spark memory iteration is proposed. The proposed algorithm first divides the data grid using an irregular dynamic density region partitioning strategy. Then, a hybrid fruit fly particle swarm algorithm based on a genetic optimization mechanism is proposed to achieve dynamic optimization seeking for parameters in local clustering to improve the clustering effect of local clustering. Finally, the local merging of samples in irregularly bounded grid cells under each partition is achieved by designing a custom clustering merging strategy. The experiments show that the improved algorithm is generally applicable to the clustering of different shape class clusters and larger scale data and has obvious improvement in accuracy and parallel efficiency.

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“…Hu et al proposed a parallel DBSCAN [17] based on genetic algorithm, which uses genetic algorithm to calculate the optimal value of neighborhood and .Wang Jun developed a parameter adaptive density clustering algorithm based on MapReduce [18] that combines the PSO method [19] to optimize the parameters of the DBSCAN algorithm in local clustering. Deng [20] proposed a parallel density clustering algorithm DPDPSO based on particle swarm optimization [21] and k-dist graph, which uses particle swarm optimization algorithm to obtain the best initial clustering center for partitioning, and uses k-dist graph to find local clustering parameters after partitioning. However, these algorithms also have a limitation that it is easy to fall into local optimization in the process of parameter optimization, so the parameter optimization ability of the algorithm needs to be further improved.…”

Section: Introductionmentioning

confidence: 99%

MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization

Liu¹,

Liu²,

Wang³

et al. 2022

Journal of Compurters

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<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.</p> <p> </p>

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Application of DBSCAN Algorithm in Data Sampling

Cited by 9 publications

References 5 publications

Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization

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