Some methods for classification and analysis of multivariate observations

By J. MacQueen. 1967

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Citations in the corpus

Exploring Simple Siamese Representation Learning (2020, methodology): “This formulation is analogous to k-means clustering [28].”
Graying the black box: Understanding DQNs (2016, background): “In order to capture the game dynamics, DQN represents a state by a sequence of history frames and pads initial states with zero frames.”
Regularizing Text Categorization with Clusters of Words (2016, methodology): “In order to extract these groups, we use a fast clustering algorithm such as K-Means (Macqueen, 1967) and especially Minibatch K-means.”
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios (2022, background): “ensure the basic vectors to be dissimilar (ideally need to be orthogonal), we initialize basic vectors with the cluster centers (by k-means [21]) of pre-trained speaker embeddings and encourage the basis vectors to be dissimilar with a regularization loss.”
Unsupervised Deep Embedding for Clustering Analysis (2015, methodology): “One branch of popular methods for clustering is k - means (MacQueen et al., 1967) and Gaussian Mixture Models (GMM) (Bishop, 2006).”
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (2017, background): “Crucially, it does not assume knowledge of the number of clusters like algorithms such as k-means [33].”
Joint Unsupervised Learning of Deep Representations and Image Clusters (2016, methodology): “We compare our approach with 12 clustering algorithms, including K-means [39], NJW spectral clustering (SCNJW) [43], self-tuning spectral clustering (SC-ST)[67], large-scale spectral clustering (SC-LS) [3], agglomerative clustering with average linkage (AC-Link)[25], Zeta function based agglomerativ”
Community detection in graphs (2009, methodology): “The most popular partitional technique in the literature is k-means clustering (MacQueen, 1967).”
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis (1987, methodology): “are many algorithms for partitioning set of objects into k clusters, such as the method [6,9,13] and the k-median approach [20]. result of such a partitioning is a list of clusters with their objects, which is not as visually appealing as the hierarchical is hoped that the graphical display…”
Tensor decompositions for learning latent variable models (2012, methodology): “We start with the simpler case where all of the covariances are identical; this probabilistic model is closely related to the (non-probabilistic) k-means clustering problem (MacQueen, 1967).”
On learning and branching: a survey (2017, methodology): “Two are the main differences between Marcos Alvarez et al. (2017) and (2016), the authors’ other attempt in developing a learned branching strategy. The explored paradigm is that of online learning. In contrast with the offline upfront previously discussed, data are now generated and learned on-the-”
Community detection in networks: A user guide (2016, background): “The resulting points can be grouped in clusters by using standard partitional clustering techniques like k-means clustering (MacQueen, 1967).”
Cross entropy-based importance sampling using Gaussian densities revisited (2019, methodology): “One such method is the k-means algorithm [19] and the further developed k-means++ algorithm [20].”