Density Peaks Clustering for Complex Datasets

Ruan, Shanshan; El‐Ashram, Saeed; Mahmood, Zahid; Mehmood, Rashid; Ahmad, Waqas

doi:10.1109/iiki.2016.20

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…This information can be used to perform various real-world applications using density peak clustering. Ruan et al [8] showed the working of density peak clustering on complex datasets.…”

Section: Literature Surveymentioning

confidence: 99%

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Devarakonda¹,

Dasari²,

Kamarajugadda³

2021

ISI

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In recent days many people are working on twitter data as the tweets are easily available and also provide reliable data. Collecting and processing these tweets produces promising and accurate results in solving many real world problems. Common problem faced by most of the people is traffic congestion. Traffic congestion results in traffic jams, mental and physical health disturbance. So to avoid this, our paper tried to show the methodology which can bring out promising results. In this paper for processing the tweet data we have used the common approach of Term Frequency-Inverse Document Frequency (TF-IDF) and discussed the application of brainstorming optimization algorithm (BSO) to avoid traffic congestion. We have also introduced the density peak clustering (DPC) to train the brain storming optimization technique. This paper has shown the modified BSO and DPC on the tweets to bring out the results which show traffic conditions at various places. We have justified our work by conducting the experiment.

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“…This information can be used to perform various real-world applications using density peak clustering. Ruan et al [8] showed the working of density peak clustering on complex datasets.…”

Section: Literature Surveymentioning

confidence: 99%

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Devarakonda¹,

Dasari²,

Kamarajugadda³

2021

ISI

View full text Add to dashboard Cite

show abstract

“…In [21], the optimal number of clusters was extracted from the results of hierarchical clustering. Furthermore, it may be more suitable for some datasets to locate a cluster by more than one density peak [26,27]. Overall speaking, making the clustering process more adaptive to the datasets with less human intervention is the goal.…”

Section: Variants Of Dpcmentioning

confidence: 99%

“…A density peak clustering (DPC) algorithm is a density-based clustering method proposed by Rodriguez and Laio [15] in 2014. Since its inception, DPC has received much attention in the research community [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Similar to other density-based methods, DPC calculates the density of each data point in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

2020

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Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

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Density Peaks Clustering for Complex Datasets

Cited by 2 publications

References 12 publications

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

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