Clustering by finding prominent peaks in density space

Li, Ni; Luo, Wenjian; Zhu, Wen-Hong; Liu, Wenjie

doi:10.1016/j.engappai.2019.07.015

Cited by 21 publications

(16 citation statements)

References 22 publications

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“…Discussion. Xie et al [39,40,44] manifest that the selection rule of dc provided in [38] cannot meet various…”

Section: Experimental Results and Analyses As Shown Inmentioning

confidence: 99%

“…We tested our algorithm and several related works, including PPC [ 44 ], DPC [ 38 ], DBSCAN [ 32 ], OPTICS [ 33 ], and AP [ 54 ], on several datasets. These datasets have different numbers of samples and stimulate different element distributions.…”

Section: Resultsmentioning

confidence: 99%

“…However, in [39][40][41][44][45][46][47], it is necessary to select the centers or observe the summary graph of density gaps, with the human operation. Gu et al [42] and Ding et al [43] proposed the strategies of automatic center selection for the original DPC, but they depend on the given appropriate dc.…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the time complexity, an alternative method based on density peaks detection using Chebyshev inequality (DPC-CI) was also given. Ni et al presented the concepts of density path and density gap, as well as a new threshold called dc percentage in [ 44 ]. The density gaps are used to draw the summary graph of density gaps calculated by several dc percentages.…”

Section: Introductionmentioning

confidence: 99%

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Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Lin

Wang

Chen

2020

Computational Intelligence and Neuroscience

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Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders. DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs). And then, subclusters are merged into individuals by comparing the density of edge samples. By iteration of the merger, the suitable dc and cluster centers are ensured. Finally, we compared state-of-the-art methods with our proposal in public datasets. Experiments show that our algorithm automatically determines cutoff distance and centers accurately.

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“…Discussion. Xie et al [39,40,44] manifest that the selection rule of dc provided in [38] cannot meet various…”

Section: Experimental Results and Analyses As Shown Inmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Lin

Wang

Chen

2020

Computational Intelligence and Neuroscience

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“…Currently, there are numerous of clustering algorithms, among which density based clustering is one of the most popular algorithms, such as DPeak [6]- [9] algorithm and DBSCAN [10] algorithm. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is a classical and one of the most important density based clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query

2020

IEEE Access

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DBSCAN is the most famous density based clustering algorithm which is one of the main clustering paradigms. However, there are many redundant distance computations among the process of DBSCAN clustering, due to brute force Range-Query used to retrieve neighbors for each point in DBSCAN, which yields high complexity (O(n 2)) and low efficiency. Thus, it is unsuitable and not applicable for large scale data. In this paper, an improved DBSCAN based on neighbor similarity is proposed, which utilizes and Cover Tree to retrieve neighbors for each point in parallel, and the triangle inequality to filter many unnecessary distance computations. From the experiments conducted on large scale data sets, it is demonstrated that the proposed algorithm greatly speedup the original DBSCAN, and outperform the main improvements of DBSCAN. Comparing with ρ-approximate DBSCAN, which is the current fastest but approximate version of DBSCAN, the proposed algorithm has two advantages: one is faster and the other is that the result is accurate. INDEX TERMS Clustering, DBSCAN, neighbor similarity, cover tree.

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Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes

et al. 2020

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Multimode is the characteristic of industrial manufacturing processes due to different production strategies and environments. For multimode process monitoring, it is a challenge to identify different steady modes and transition modes. In this paper, a k nearest neighbours (KNN)‐based density peaks clustering (DPC) method is applied to identify different modes. First, the local density of each sample, which is obtained with a KNN constraint and its minimum distance to the higher local density points are calculated as two indicators of the DPC algorithm to find the cluster centres of the training data. Then, the transition modes are identified by combining the moving window strategy and the DPC algorithm, where an index called the local density‐distance ratio (LDDR) is employed. Finally, the monitoring algorithm is used to detect the faults for each operation mode. The effectiveness and advantages of the proposed method are illustrated by a numerical example and a Tennessee Eastman (TE) benchmark process.

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Clustering by finding prominent peaks in density space

Cited by 21 publications

References 22 publications

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query

Density peaks clustering‐based steady/transition mode identification and monitoring of multimode processes

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