Clustering by fast search and find of density peaks

By Alex Rodriguez, Alessandro Laio. Science, 2014

3,435 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1faa856958fd22125dff44c40d5fd7ba92e7cb3b

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Spike sorting with Kilosort4 (2024, positive): “Some frameworks 1–3 took advantage of new clustering algorithms such as density-based approaches 4 or agglomerative approaches using bimodality criteria 5 .”
Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, methodology): “To this end, we conducted PCA and t-SNE (the first 10 PCs was used) based on the genes expressed in at least 10% of all the cells of each tumor, and further applied density peak clustering method to identify clusters in the t-SNE space (Rodriguez and Laio, 2014).”
Feature selection in machine learning: A new perspective (2018, methodology): “SVM, BP-NN, RBF-NN, K-means, Hierarchical clustering, Density based clustering and so on [60-65].”
Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning (2018, methodology): “Specifically, we perform density based clustering [31] to form a single cluster of normal instances within each object class independently, and instances outside this cluster are considered outliers.”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, methodology): “The CFSFDP algorithm is fast and able to recognize clusters regardless of their shape.”
Deep Learning Face Attributes in the Wild (2014, methodology): “Similar to [24], a fast density peak identification technique is devised.”
Unsupervised K-Means Clustering Algorithm (2020, methodology): “Furthermore, the comparisons of the proposed U-k-means with k-means [8], robust EM [31], clustering by fast search (C-FS) [30], X-means [23], and RL-FCM [33] are also made.”
Deep Self-Learning From Noisy Labels (2019, methodology): “One is the density peak by Euclidean distance [29], while the other is the [15] 83.47 3 CleanNet w soft [15] 83.95 4 Ours 85.11 Table 5.”
Spike sorting for large, dense electrode arrays (2015, neutral): “33, 34 ).”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, methodology): “Like DBSCAN and the mean-shift method [8] , density peaks clustering (DPC) algorithm [9] proposed by Rodriguez and Laio is able to detect non-spherical clusters and does not require one to specify the number of clusters.”
Data-driven Automatic Treatment Regimen Development and Recommendation (2016, methodology): “Density-Peaks-based Clustering (DPC) is a recently proposed exemplar-based clustering algorithm [18], which can discover clusters with complex shapes.”