Clustering of Multiple Density Peaks

Cai, Borui; Huang, Guangyan; Xiang, Yong; He, Jing; Huang, Guang-Li; Deng, Ke; Zhou, Xiangmin

doi:10.1007/978-3-319-93040-4_33

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Another notable clustering algorithm is Density Peak Clustering (DPC), which identifies cluster centers based on their density and assigns points to clusters accordingly (Rodriguez & Laio 2014). Several improved versions of DPC, such as MDPC, PPC, FDP Cluster, and DPCG, have been proposed (Cai et al 2018;Ni et al 2019;Yan et al 2016;Xu et al 2016). However, these algorithms tend to select high-density points as initial cluster centers, which may lead to incorrect assignments or treat low-density points as noise.…”

Section: B Related Workmentioning

confidence: 99%

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

ELFarra,

Salaha,

Ashour

2024

Data Science Journal

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Clustering is a fundamental technique in data mining and machine learning, aiming to group data elements into related clusters. However, traditional clustering algorithms, such as K-means, suffer from limitations such as the need for user-defined parameters and sensitivity to initial conditions. This paper introduces a novel clustering algorithm called Black Hole Clustering (BHC), which leverages the concept of gravity to identify clusters. Inspired by the behavior of masses in the physical world, gravity-based clustering treats data points as mass points that attract each other based on distance. This approach enables the detection of high-density clusters of arbitrary shapes and sizes without the need for predefined parameters. We extensively evaluate BHC on synthetic and real-world datasets, demonstrating its effectiveness in handling complex data structures and varying point densities. Notably, BHC excels in accurate prediction of the number of clusters and achieves competitive clustering accuracy rates. Moreover, its parameter-free nature enhances clustering accuracy, robustness, and scalability. These findings represent a significant contribution to advanced clustering techniques and pave the way for further research and application of gravity-based clustering in diverse fields. BHC offers a promising approach to addressing clustering challenges in complex datasets, opening up new possibilities for improved data analysis and pattern discovery.

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Section: B Related Workmentioning

confidence: 99%

Black Hole Clustering: Gravity-Based Approach with No Predetermined Parameters

ELFarra,

Salaha,

Ashour

2024

Data Science Journal

View full text Add to dashboard Cite

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“…Thus, the cluster centre can be identified first, allowing the assignment of a point to a cluster according to the distance of other points from different cluster centres [15]. Many improved clustering algorithms, such as multiple DPC (MDPC) [16], PPC [17], FDPCluster [18] and DPCG [19], have been developed on the basis of DPC.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Density Clustering Algorithm Based on Similarity for Dataset With Density Variation

Zhou

Zhang

et al. 2019

IEEE Access

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Clustering has been widely used in the fields of knowledge discovery, pattern recognition and artificial intelligence. However, discovering clusters in spatial databases is still a challenging task, especially when the shape, size, and density of clusters vary a lot. Existing algorithms have sensitive parameters, clusters must be separated far enough from each other and rich prior knowledge about datasets is required. In this paper, we propose algorithm DENSS, which performs clustering on the basis of the similarity of neighbour distribution and the number of shared neighbors for two objects. Algorithm DENSS can mine clusters that differ in densities, and within a cluster the local densities are reasonably homogeneous. Adjacent objects are separated into different clusters by significant change in densities. To verify the effectiveness of the algorithm DENSS, synthetic and real-world datasets are used for testing, and it has been compared with seven clustering algorithms. Experimental results show that the proposed algorithm has a relatively high efficiency, robustness and effectiveness, and is remarkably superior to the seven algorithms. This algorithm is universal and can rapidly and efficiently identify the clusters of different densities, shapes and sizes even in the presence of noise and outliers for any object feature types. INDEX TERMS Similarity measurement, multi-density clustering, arbitrary shaped clustering, varied density.

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