k-means++

k-means++: The advantages of careful seeding. The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.


References in zbMATH (referenced in 176 articles , 1 standard article )

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  1. Brunet-Saumard, Camille; Genetay, Edouard; Saumard, Adrien: K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means (2022)
  2. Deng, Nan; Noack, Bernd R.; Morzyński, Marek; Pastur, Luc R.: Cluster-based hierarchical network model of the fluidic pinball -- cartographing transient and post-transient, multi-frequency, multi-attractor behaviour (2022)
  3. Ermağan, Umut; Yıldız, Barış; Salman, F. Sibel: A learning based algorithm for drone routing (2022)
  4. Grandoni, Fabrizio; Ostrovsky, Rafail; Rabani, Yuval; Schulman, Leonard J.; Venkat, Rakesh: A refined approximation for Euclidean (k)-means (2022)
  5. Murphy, James M.; Polk, Sam L.: A multiscale environment for learning by diffusion (2022)
  6. Sánchez Pérez de Amézaga, Claudio; García-Suárez, Víctor M.; Fernández-Martínez, Juan L.: Classification and prediction of bulk densities of states and chemical attributes with machine learning techniques (2022)
  7. Sembach, Lena; Burgard, Jan Pablo; Schulz, Volker: A Riemannian Newton trust-region method for fitting Gaussian mixture models (2022)
  8. Tan, Xiao Jian; Mustafa, Nazahah; Mashor, Mohd Yusoff; Ab Rahman, Khairul Shakir: Automated knowledge-assisted mitosis cells detection framework in breast histopathology images (2022)
  9. Valdés-Alonzo, Gabriel; Binetruy, Christophe; Eck, Benedikt; García-González, Alberto; Leygue, Adrien: Phase distribution and properties identification of heterogeneous materials: a data-driven approach (2022)
  10. Yang, Jun; He, Ping; Fang, Kai-Tai: Three kinds of discrete approximations of statistical multivariate distributions and their applications (2022)
  11. Bertsimas, Dimitris; Orfanoudaki, Agni; Wiberg, Holly: Interpretable clustering: an optimization approach (2021)
  12. Boubekki, Ahcène; Kampffmeyer, Michael; Brefeld, Ulf; Jenssen, Robert: Joint optimization of an autoencoder for clustering and embedding (2021)
  13. Boutalbi, Rafika; Labiod, Lazhar; Nadif, Mohamed: Implicit consensus clustering from multiple graphs (2021)
  14. Brécheteau, Claire; Fischer, Aurélie; Levrard, Clément: Robust Bregman clustering (2021)
  15. Coelho, André L. V.; Sandes, Nelson C.: Data clustering via cooperative games: a novel approach and comparative study (2021)
  16. Deshpande, Amit; Pratap, Rameshwar: Sampling-based dimension reduction for subspace approximation with outliers (2021)
  17. Gangloff, Hugo; Courbot, Jean-Baptiste; Monfrini, Emmanuel; Collet, Christophe: Unsupervised image segmentation with Gaussian pairwise Markov fields (2021)
  18. Giffon, Luc; Emiya, Valentin; Kadri, Hachem; Ralaivola, Liva: Quick-means: accelerating inference for K-means by learning fast transforms (2021)
  19. Gruzdeva, Tatiana V.; Ushakov, Anton V.: K-means clustering via a nonconvex optimization approach (2021)
  20. Ibrahim, Mohamed Hamza; Missaoui, Rokia: An exemplar-based clustering using efficient variational message passing (2021)

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