A method of two-stage clustering learning based on improved DBSCAN and density peak algorithm.

As an application in density-based clustering methods, FPCAP method was combined with the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. ... Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning since they can deal with non-hyperspherical clusters and are robustness to handle ... Density-based clustering is one of the most important clustering methods. It is based on density estimation of data points and defines clusters as dense regions separated by low dense regions. ...

arXiv:2109.11383v2 fatcat:4ja26j4sojd2hcaczk5kqskdae

Multiple Versions

Density peak clustering (DPC) algorithm is to find clustering centers by calculating the local density and distance of data points based on the distance between data points and the cutoff distance (d c ... For this reason, a clustering algorithm which combines teaching and learning optimization algorithm and density gap is proposed (NSTLBO-DGDPC). ... Preliminaries Density Peak Clustering Algorithm DPC The algorithm is based on two simple and intuitive assumptions. ...

doi:10.1007/s42979-020-00183-2 fatcat:3chzjrtse5dnfhagh5t3x5en3y

Open Access

The density peaks clustering (DPC) algorithm is a novel density-based clustering approach. ... A density fragment clustering (DFC) algorithm without peaks algorithm is proposed with inspiration from DPC, DBSCAN and SCAN to cope with a larger number of data sets. ... Acknowledgments The authors are grateful to the financial support by the National Natural Science Foundation of China ...

doi:10.3233/jifs-17678 fatcat:mgi3dyiwcnd4hoq3jmtqhnzvcm

To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). ... Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical ... Liu [14] designed a new adaptive density peak clustering method based on the K-nearest neighbors (ADPC-KNN) and redefined the local density calculation method. ...

doi:10.1109/access.2020.3006069 fatcat:dq6mvgxmdjdyxh3i36i7i6l75y

DOAJ

One of the common techniques to cluster documents is the density-based clustering algorithms using the density of data points as a main strategic to measure the similarity between them. ... The delivered review revealed that the most used density-based algorithms in document clustering are DBSCAN and DPC. ... ., [59] proposed a new algorithm based on the density of k-means, which was cluster center initialization based on density peak (CCIDP). ...

doi:10.11591/ijeecs.v22.i1.pp552-562 fatcat:ktlnznp5evelxhgaef2lmqzfm4

Open Access

To address this limitation, we propose a novel density based clustering algorithm called the Density Propagation based Adaptive Multi-density clustering (DPAM) algorithm. ... The performance of density based clustering algorithms may be greatly influenced by the chosen parameter values, and achieving optimal or near optimal results very much depends on empirical knowledge obtained ... DBSCAN is a typical density based clustering algorithm, and in DBSCAN the density of every point is associated with the number of points within a threshold radius circle. ...

doi:10.1371/journal.pone.0198948 pmid:30020928 pmcid:PMC6051564 fatcat:w4w67hghmnd4vlt6kwoqvyucoe

DOAJ

Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset based on their local densities. ... The proposed method named feasible residual error-based density peak clustering (FREDPC) algorithm with the fragment merging strategy only needs to perform in one single step without any iteration and ... Recently, Rodriguez and Liao proposed a density-based clustering algorithm called density peak clustering (DPC) [33] , which adopted the idea of local density maxima from the mean-shift method [34] ...

doi:10.1109/access.2019.2926579 fatcat:pzr4ulerpjhqflwaevmpyeb4ny

DOAJ

Based on the density-based spatial clustering of applications with noise (DBSCAN), this paper designs the hierarchical DBSCAN clustering algorithm to identify and analyze the categories of poverty-stricken ... Based on the idea of division and aggregation, the proposed method can recognize clusters of different forms and deal with noises effectively in the data space with imbalanced density distribution. ... aggregation to design a clustering algorithm named hierarchical DBSCAN (HDBSCAN). e proposed method comprises two stages of division and aggregation. ...

doi:10.1155/2021/6692975 fatcat:qo5uyhntwrcahencayn2iofoh4

DOAJ

Spark is one of the most popular parallel processing platforms for big data, and many researchers have proposed many parallel clustering algorithms based on Spark. ... In this paper, the existing parallel clustering algorithms based on Spark are classified and summarized, the parallel design framework of each kind of algorithms is discussed, and after comparing different ... Conflicts of Interest e authors declare that there are no conflicts of interest in connection with the work submitted. ...

doi:10.1155/2020/8884926 fatcat:24pcgfyhuffl3mrwxtf4kjeenu

DOAJ

Based on the a priori density distribution of the points one large region is built (Fig. 12, a) , and in Fig. 12 , b there are two regions. ... Annually old algorithms are modernized and improved both a new ways and approaches of clustering techniques are investigated. ...

doi:10.15587/1729-4061.2015.39785 fatcat:lczgl35r5ba5zourzh76r4cg2m

DOAJ OJS

This paper presents a novel two-phase parallel hybrid clustering (HPPD) algorithm that identify convex and non-convex groups in online stage and mixed groups in offline stage from data stream. ... However, the main attention of research on clustering methods till now has been concerned with alteration of the methods updated for static datasets and changes of the available modified methods. ... This method was inspired by the density-based clustering and self-adaptive peak density clustering algorithms for data with mixed attributes. ...

doi:10.33899/csmj.2020.164677 fatcat:bzwuvj6xxfemxebncqhy7xf5ay

DOAJ

Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN ... The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time. ... Author Contributions: S.W. was responsible for the concept, design and most of the writing and review of the manuscript, and participated in the linear discriminant analysis, density-based clustering algorithms ...

doi:10.3390/agriculture10100465 fatcat:hktiqbhqhfbhbi53ilgsd3rrbu

DOAJ

Based on the idea of density clustering, Liu et al. proposed a spatiotemporal expansion method based on DBSCAN with location data. ... analysed the shortcomings of the DBSCAN (density-based noise application spatial clustering) method in hot area recognition and proposed a method of hot spot automatic generation based on fuzzy density ...

doi:10.1109/access.2021.3075682 fatcat:ypirh4l6u5aztfc774xh256mua

DOAJ

The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based ... As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering ... This work is supported by University of Malaya High Impact Research Grant no vote UM.C/625/HIR/MOHE/SC/13/2 from Ministry of Education Malaysia. ...

doi:10.1007/978-3-319-09156-3_49 fatcat:fzqmgxiul5gj5awkt6nzbwa4wm

Besides, in order to select the cluster centers automatically, a two-phase exemplar determination method is proposed. ... The experiments on several synthetic and real-world datasets show that the proposed algorithm has higher clustering effectiveness and better robustness for data with varying density, multi-scale and noise ... Other density-based methods, such as DBSCAN [5] , are able to detect non-spherical clusters using a predefined density threshold. They can easily find clusters with different sizes and shapes. ...

doi:10.1145/2851613.2851644 dblp:conf/sac/ZhangPPE16 fatcat:2yh4x76evjcwvoxskkd4pc6rdy

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Clustering Algorithm of Density Difference Optimized by Mixed Teaching and Learning

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DFC: Density Fragment Clustering without Peaks

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Effective Density Peaks Clustering Algorithm Based On the Layered K-Nearest Neighbors And Subcluster Merging

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