Clustering by Fast Search and Find of Density Peaks

By Thomas Lin Pedersen, S. Hughes. 2016

2,092 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/86e07d015376035f9a554535ca9f1988f0947f25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Weiwei Sun
2. Hannah A. Pliner
3. Jia Wu
4. Tao Lei
5. Xiaojie Qiu
6. Dongkuan Xu
7. Shan Xue
8. Xiaohong Jia

Citations in the corpus

• A Comprehensive Survey of Clustering Algorithms (2015, methodology): “7 Clustering Algorithm Based on Density and Distance DD (Density and distance-based clustering) is another significant clustering algorithm proposed in Science in 2014 [118], of which the core idea is novel.”
• Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, methodology): “Clustering is the process of grouping objects together according to their similarities, so that objects in the same cluster 2 are similar to one another and dissimilar to objects in any other cluster [12,15,18,27,30,33] .”
• CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (2018, unknown): “Inspired by recent clustering algorithms described in [29], we conduct the following procedures in each category .”
• Unsupervised K-Means Clustering Algorithm (2020, unknown): “We next compare the proposed U-k-means algo-rithm with R-EM, C-FS, k-means with true c , X-means, and RL-FCM.”
• Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “The recently proposed clustering method in [193] has been used in [29]; the progress in deep learning in [194] has been used in [195].”
• A Comprehensive Survey on Community Detection With Deep Learning (2021, methodology): “LCCD particularly uses the density peak clustering algorithm [55] to locate the structural centres from networks and then expands communities from the identified centres to the borders through a local search procedure.”
• Reversed graph embedding resolves complex single-cell developmental trajectories (2017, methodology): “We then identify clusters of cells using density peak clustering 9,10 ​ , an efficient, highly scalable one-step clustering algorithm.”
• Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation (2019, methodology): “In the future work, we will explore fast clustering algorithms that automatically estimate the number of clusters [48], [49].”
• Automated Mapping of Phenotype Space with Single-Cell Data (2016, unknown): “Identification of local density maxima has been addressed previously by a number of mode-seeking algorithms (e.g. mean-shift 21 , quick-shift 22 and the recent algorithm by Rodriguez and Laio 23 ).”
• SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference (2024, unknown): “Then, we group ¯ h v tokens with k -nearest neighbor density peak aggregation algorithm (Rodriguez, 2014) for adaptive token aggregation.”
• Hyperspectral Band Selection: A Review (2019, methodology): “Inspired by the fast density peak-based clustering (FDPC) algorithm in [29], a ranking-based exemplar component analysis algorithm was proposed to automatically find cluster centers from all the bands.”
• Semantic Clustering and Convolutional Neural Network for Short Text Categorization (2015, unknown): “Firstly, the fast clustering algorithm (Rodriguez and Laio, 2014), based on searching density peaks, is utilized to cluster word embeddings and discover semantic cliques, as shown in Figure 1.”