Clustering by Passing Messages Between Data Points

By Brendan J. Frey, Delbert Dueck. 2007

7,080 citations recorded by the source corpus.

Subfields: Molecular Biology, Probabilistic & Bayesian Methods

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Citations in the corpus

subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs (2016, methodology): “Subsequently, relational data clustering algorithms such as Affinity Propagation (AP) [16] and Hierarchical Clustering could be used to cluster the graphs.”
Deep Metric Learning via Lifted Structured Feature Embedding (2015, methodology): “For the experiments, we split 59,551 images of 11,318 classes for training and 60,502 images of 11,316 classes for testing.”
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018, background): “…et al., 2016, Ribeiro et al., 2016, Lei et al., 2016], generalized additive models [Caruana et al., 2015], procedures [Singh et al., 2016], falling rule lists [Wang and Rudin, 2015], exemplars [Kim et al., 2014, Frey and Dueck, 2007] and decision sets [Lakkaraju et al., 2016]—to name a few.”
graph2vec: Learning Distributed Representations of Graphs (2017, methodology): “Given G , in graph clustering, the goal is to group similar graphs together. graph2vec ’s embeddings could be used along with general purpose clustering algorithms such as K-means and relational clustering algorithms such as Aﬃnity Propagation (AP) [14] to achieve this.”
A synaptic organizing principle for cortical neuronal groups (2011, methodology): “We first identified clusters using an affinity propagation algorithm (62) in which affinity was defined by the number of common neighbors.”
Unsupervised Joint Mining of Deep Features and Image Labels for Large-Scale Radiology Image Categorization and Scene Recognition (2017, methodology): “The Affinity Propagation algorithm [19] (AP) is then invoked to perform a “tuning parameter-free” partitioning on this pairwise affinity matrix {Ai,j} ∈ RK×K .”
Overlapping community detection in networks: The state-of-the-art and comparative study (2011, methodology): “The probability for assigning node i into the cluster represented by exemplar node k is computed by equation p ( i, k ) = e ˆ r ( i,k ) , where ˆ r is the normalized responsibility as in [Geweniger et al. 2009].”
Data Mining Methods for Recommender Systems (2015, background): “Affinity Propagation is an important representative of this family of algorithms [33] that works by defining two kinds of messages between nodes: “responsibility”, which reflects how well-suited receiving point is”
Clustering and Community Detection in Directed Networks: A Survey (2013, background): “[85] the authors combined random walks with the concept of affinity propagation [86] and proposed a message passing algorithm for community detection in both directed and undirected networks.”
APCluster: an R package for affinity propagation clustering (2011, methodology): “AP clustering has been used successfully for clustering microarray/gene expression data (Frey and Dueck, 2007; Kiddle et al., 2010; Leone et al., 2007; Liu et al., 2010; Tang et al., 2010), in structural biology (North et al., 2011; Pandit and Skolnick, 2010; Wang et al., 2010), biological network…”