An Implementation of the HDBSCAN* Clustering Algorithm

Stewart, Geoffrey; Al-khassaweneh, Mahmood

doi:10.3390/app12052405

Cited by 34 publications

(22 citation statements)

References 13 publications

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“…Otherwise, n remains unlabelled. After this process, we cluster the remaining nodes using the HDBSCAN [14] algorithm and add the results to…”

Section: Nei N C Ten N Cmentioning

confidence: 99%

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Wang,

Jia,

Yang

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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Network clustering for protein complex identification within protein interaction networks has long been a focal point for researchers in the field of machine learning and data mining. Although existing algorithms take into account the dynamic properties of protein complexes, they ignore the computational dynamics. This paper introduces a novel network clustering algorithm designed for dynamic protein complex detection. This approach integrates gene expression data and static network data to construct a dynamic network construction model and incorporates multiple data sources to formulate a node representation model. It devises a new dynamic clustering model that simulates the splitting, merging, and growth of protein complexes, providing a framework for dynamic network clustering. Experimental results demonstrate that the proposed algorithm outperforms dynamic clustering algorithms and traditional clustering algorithms.

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“…Otherwise, n remains unlabelled. After this process, we cluster the remaining nodes using the HDBSCAN [14] algorithm and add the results to…”

Section: Nei N C Ten N Cmentioning

confidence: 99%

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Wang,

Jia,

Yang

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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“…It does not require each data point to be assigned to a cluster, as it recognises dense clusters. Outliers or noise are points belonging to no cluster group (Stewart & Al-Khassaweneh, 2022).…”

Section: Detection Of Shc Clustersmentioning

confidence: 99%

Land use and sexual harassment: A geospatial analysis based on the volunteer HarassMap‐Egypt

Al‐Sabbagh,

Li,

Lee

et al. 2023

Geographical Research

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Sexual harassment and gang rape in Egypt have garnered attention from both traditional and digital media. This study employed a volunteer HarassMap to analyse sexual harassment crimes (SHCs) across Egypt from a spatial perspective. The specific aims were to apply the Hierarchical Density‐Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to locate clusters of reported SHCs, and to assess their spatial dependence on land use types. To accomplish this task, ring buffers of 100, 200, 300, 400, and 500 metres were established around each crime scene to determine which land use was mostly associated with the incidence of these SHCs. Local bivariate relationships were used to explore the associations between SHC and each land‐use category. Results from the HDBSCAN algorithm revealed four crime clusters within the study domain, mainly located in Greater Cairo, Alexandria, and Behaira. Notably, commercial establishments and transit stations showed a significantly positive correlation with SHC. The study shows how land uses shape SHC and showed that it is possible to identify environmental risk factors for harassment. These risk factors can help policymakers, urban planners, and community stakeholders prevent and reduce sexual harassment and gender inequality, and promote just and inclusive societies.

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“…From the tuned model (mtry = 15), we extracted the most important morpho-colorimetric variables and the confusion matrix. Finally, we applied HDBSCAN* (Hierarchical Density-Based Spatial Clustering of Applications with Noise) [81,82], an unsupervised clustering algorithm. Classical clustering techniques such as K-mean are limited by the fact that (1) the number of clusters must be known a priori, (2) each point, even outliers, must belong to a cluster, and lastly (3) they assume some known probability density function (PDF) that may have generated the observed data.…”

Section: Seed Morphometric Analysismentioning

confidence: 99%

Integrative Taxonomy of Armeria Taxa (Plumbaginaceae) Endemic to Sardinia and Corsica

Tiburtini,

Bacchetta,

Sarigu

et al. 2023

Plants

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Sardinia and Corsica are two Mediterranean islands where the genus Armeria is represented by 11 taxa, 10 out of which are endemic. An integrative approach, using molecular phylogeny, karyology, and seed and plant morphometry was used to resolve the complex taxonomy and systematics in this group. We found that several taxa are no longer supported by newly produced data. Accordingly, we describe a new taxonomic hypothesis that only considers five species: Armeria leucocephala and A. soleirolii, endemic to Corsica, and A. morisii, A. sardoa, and A. sulcitana, endemic to Sardinia.

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An Implementation of the HDBSCAN* Clustering Algorithm

Cited by 34 publications

References 13 publications

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Network clustering algorithm for dynamic protein complex detection fused graph embedding

Land use and sexual harassment: A geospatial analysis based on the volunteer HarassMap‐Egypt

Integrative Taxonomy of Armeria Taxa (Plumbaginaceae) Endemic to Sardinia and Corsica

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