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TheK-Means Algorithm Evolution
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cite.330470215openalex-snapshot
Automatkche Klassifikation. Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten. (Cluster‐Analyse). Von H. H. Bock. Vandenhoeck & Ruprecht, Gottingen‐Zürich 1974. 1. Aufl., 480 S., 54 Abb., Ln. DM 82,–
inferredcites →https://doi.org/10.1006/jcom.2001.0633openalex-snapshot
The New k-Windows Algorithm for Improving thek -Means Clustering Algorithm
inferredcites →https://doi.org/10.1007/3-540-45643-0_13openalex-snapshot
Acceleration of K-Means and Related Clustering Algorithms
inferredcites →https://doi.org/10.1007/978-3-319-09259-1_2openalex-snapshot
Accelerating Lloyd’s Algorithm for k-Means Clustering
inferredcites →https://doi.org/10.1007/978-3-319-14998-1_17openalex-snapshot
Fast Approximate $$K$$-Means via Cluster Closures
inferredcites →https://doi.org/10.1007/978-3-319-49487-6_3openalex-snapshot
Theoretical Analysis of the k-Means Algorithm – A Survey
inferredcites →https://doi.org/10.1007/978-3-319-91189-2_21openalex-snapshot
MIC-KMeans: A Maximum Information Coefficient Based High-Dimensional Clustering Algorithm
inferredcites →https://doi.org/10.1007/978-3-540-74484-9_58openalex-snapshot
Improving the Efficiency and Efficacy of the K-means Clustering Algorithm Through a New Convergence Condition
inferredcites →https://doi.org/10.1007/978-3-642-10665-1_71openalex-snapshot
Parallel K-Means Clustering Based on MapReduce
inferredcites →https://doi.org/10.1007/978-3-642-21937-5_11openalex-snapshot
Two-Stage Clustering with k-Means Algorithm
inferredcites →https://doi.org/10.1007/978-3-642-39515-4_13openalex-snapshot
An Optimized Method for Selection of the Initial Centers of K-Means Clustering
inferredcites →https://doi.org/10.1007/bf00987153openalex-snapshot
The dynamic clusters method in nonhierarchical clustering
inferredcites →https://doi.org/10.1007/s10115-007-0114-2openalex-snapshot
Top 10 algorithms in data mining
inferredcites →https://doi.org/10.1007/s10994-009-5103-0openalex-snapshot
NP-hardness of Euclidean sum-of-squares clustering
inferredcites →https://doi.org/10.1007/s13042-012-0079-7openalex-snapshot
A hybrid approach to speed-up the k-means clustering method
inferredcites →https://doi.org/10.1016/0167-8655(93)90058-lopenalex-snapshot
A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm
inferredcites →https://doi.org/10.1016/0167-8655(95)00119-0openalex-snapshot
New methods for the initialisation of clusters
inferredcites →https://doi.org/10.1016/c2013-0-06161-0openalex-snapshot
Cluster Analysis for Applications
inferredcites →https://doi.org/10.1016/j.asoc.2011.11.008openalex-snapshot
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
inferredcites →https://doi.org/10.1016/j.asoc.2017.08.032openalex-snapshot
Two improved k-means algorithms
inferredcites →https://doi.org/10.1016/j.compeleceng.2012.11.013openalex-snapshot
Automated two-dimensional K-means clustering algorithm for unsupervised image segmentation
inferredcites →https://doi.org/10.1016/j.cor.2005.11.023openalex-snapshot
K-T.R.A.C.E: A kernel -means procedure for classification
inferredcites →https://doi.org/10.1016/j.csda.2004.06.017openalex-snapshot
Optimising -means clustering results with standard software packages
inferredcites →https://doi.org/10.1016/j.eswa.2018.09.006openalex-snapshot
A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data
inferredcites →https://doi.org/10.1016/j.future.2018.04.025openalex-snapshot
Category Preferred Canopy–K-means based Collaborative Filtering algorithm
inferredcites →https://doi.org/10.1016/j.imavis.2010.10.002openalex-snapshot
Improving the performance of k-means for color quantization
inferredcites →https://doi.org/10.1016/j.ins.2010.10.008openalex-snapshot
A time-efficient pattern reduction algorithm for k-means clustering
inferredcites →https://doi.org/10.1016/j.ins.2013.05.023openalex-snapshot
Fast global k-means clustering based on local geometrical information
inferredcites →https://doi.org/10.1016/j.jpdc.2014.01.003openalex-snapshot
Trends in big data analytics
inferredcites →https://doi.org/10.1016/j.knosys.2013.11.010openalex-snapshot
Analysis of the k-means algorithm in the case of data points occurring on the border of two or more clusters
inferredcites →https://doi.org/10.1016/j.knosys.2014.04.027openalex-snapshot
A heuristic approach to effective and efficient clustering on uncertain objects
inferredcites →https://doi.org/10.1016/j.knosys.2018.01.031openalex-snapshot
Improved K-means algorithm based on density Canopy
inferredcites →https://doi.org/10.1016/j.patcog.2008.06.005openalex-snapshot
Improvement of the k-means clustering filtering algorithm
inferredcites →https://doi.org/10.1016/j.patcog.2009.02.014openalex-snapshot
A fast -means clustering algorithm using cluster center displacement
inferredcites →https://doi.org/10.1016/j.patcog.2009.11.021openalex-snapshot
Fast global k-means clustering using cluster membership and inequality
inferredcites →https://doi.org/10.1016/j.patcog.2010.10.018openalex-snapshot
Fast modified global k-means algorithm for incremental cluster construction
inferredcites →https://doi.org/10.1016/j.patrec.2004.04.007openalex-snapshot
Cluster center initialization algorithm for K-means clustering
inferredcites →https://doi.org/10.1016/j.patrec.2007.01.001openalex-snapshot
A method for initialising the K-means clustering algorithm using kd-trees
inferredcites →https://doi.org/10.1016/j.patrec.2008.02.014openalex-snapshot
An efficient k′-means clustering algorithm
inferredcites →https://doi.org/10.1016/j.patrec.2009.09.011openalex-snapshot
Data clustering: 50 years beyond K-means
inferredcites →https://doi.org/10.1016/j.proeng.2011.11.2650openalex-snapshot
Cluster Center Initialization Method for K-means Algorithm Over Data Sets with Two Clusters
inferredcites →https://doi.org/10.1016/j.tcs.2010.05.034openalex-snapshot
The planar k -means problem is NP-hard
inferredcites →https://doi.org/10.1016/s0031-3203(02)00060-2openalex-snapshot
The global k-means clustering algorithm
inferredcites →https://doi.org/10.1016/s1007-0214(05)70069-9openalex-snapshot
Improvement and parallelism of k-means clustering algorithm
inferredcites →https://doi.org/10.1037/1082-989x.8.3.294openalex-snapshot
Local Optima in K-Means Clustering: What You Don't Know May Hurt You.
inferredcites →https://doi.org/10.1051/jphystap:019020010017801openalex-snapshot
Comptes rendus de l'Académie des Sciences
inferredcites →https://doi.org/10.1063/1.4913021openalex-snapshot
An improvement to the K-means algorithm oriented to big data
inferredcites →https://doi.org/10.1063/1.4913023openalex-snapshot
Fast means: Enhancing the K-means algorithm by accelerating its early classification version
inferredcites →https://doi.org/10.1063/1.4952103openalex-snapshot
The early stop heuristic: A new convergence criterion for K-means
inferredcites →https://doi.org/10.1093/biomet/18.1-2.105openalex-snapshot
ON THE COEFFICIENT OF RACIAL LIKENESS
inferredcites →https://doi.org/10.1109/97.329844openalex-snapshot
A new initialization technique for generalized Lloyd iteration
inferredcites →https://doi.org/10.1109/97.551685openalex-snapshot
Modified K-means algorithm for vector quantizer design
inferredcites →https://doi.org/10.1109/cisp.2008.350openalex-snapshot
Improved K-Means Clustering Algorithm
inferredcites →https://doi.org/10.1109/eait.2011.57openalex-snapshot
Enhancing the K-means Clustering Algorithm by Using a O(n logn) Heuristic Method for Finding Better Initial Centroids
inferredcites →https://doi.org/10.1109/etcs.2010.347openalex-snapshot
A Simple and Fast Algorithm for Global K-means Clustering
inferredcites →https://doi.org/10.1109/icccbda.2016.7529538openalex-snapshot
Enhancing parallel k-means using map reduce for discovering knowledge from big data
inferredcites →https://doi.org/10.1109/icdm.2001.989523openalex-snapshot
An efficient Fuzzy C-Means clustering algorithm
inferredcites →https://doi.org/10.1109/icmlc.2004.1382371openalex-snapshot
A new algorithm to get the initial centroids
inferredcites →https://doi.org/10.1109/icrtit.2011.5972376openalex-snapshot
Data clustering with modified K-means algorithm
inferredcites →https://doi.org/10.1109/icsmc.2007.4413608openalex-snapshot
A Time efficient Pattern Reduction algorithm for k-means based clustering
inferredcites →https://doi.org/10.1109/ieeeconf13168.2007openalex-snapshot
2007 International Conference on Computational Science and its Applications (ICCSA 2007)
inferredcites →https://doi.org/10.1109/nabic.2012.6402258openalex-snapshot
Improvement to the K-Means algorithm through a heuristics based on a bee honeycomb structure
inferredcites →https://doi.org/10.1109/tisc.2010.5714605openalex-snapshot
An efficient K-Means clustering algorithm for reducing time complexity using uniform distribution data points
inferredcites →https://doi.org/10.1109/tit.1982.1056489openalex-snapshot
Least squares quantization in PCM
inferredcites →https://doi.org/10.1109/tkde.2008.88openalex-snapshot
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
inferredcites →https://doi.org/10.1109/tpami.1984.4767478openalex-snapshot
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
inferredcites →https://doi.org/10.1109/tpami.2002.1017616openalex-snapshot
An efficient k-means clustering algorithm: analysis and implementation
inferredcites →https://doi.org/10.1109/tpami.2006.66openalex-snapshot
A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering
inferredcites →https://doi.org/10.1109/tpami.2011.255openalex-snapshot
Optimized Data Fusion for Kernel k-Means Clustering
inferredcites →https://doi.org/10.1111/j.2517-6161.1948.tb00008.xopenalex-snapshot
The Utilization of Multiple Measurements in Problems of Biological Classification
inferredcites →https://doi.org/10.1145/347090.347123openalex-snapshot
Efficient clustering of high-dimensional data sets with application to reference matching
inferredcites →https://doi.org/10.1186/s40537-015-0030-3openalex-snapshot
Big data analytics: a survey
inferredcites →https://doi.org/10.1201/9781420090741.ch2openalex-snapshot
Pattern Recognition Principles
inferredcites →https://doi.org/10.1243/0954406041319509openalex-snapshot
An Incremental K-means algorithm
inferredcites →https://doi.org/10.1243/095440605x8298openalex-snapshot
Selection of K in K -means clustering
inferredcites →https://doi.org/10.1348/000711005x48266openalex-snapshot
K‐means clustering: A half‐century synthesis
inferredcites →https://doi.org/10.1371/journal.pone.0049946openalex-snapshot
Reducing the Time Requirement of k-Means Algorithm
inferredcites →https://doi.org/10.1371/journal.pone.0162259openalex-snapshot
What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm
inferredcites →https://doi.org/10.1371/journal.pone.0201874openalex-snapshot
Balancing effort and benefit of K-means clustering algorithms in Big Data realms
inferredcites →https://doi.org/10.1631/jzus.2006.a1626openalex-snapshot
An efficient enhanced k-means clustering algorithm
inferredcites →https://doi.org/10.1631/jzus.c1200078openalex-snapshot
An accelerated K-means clustering algorithm using selection and erasure rules
inferredcites →https://doi.org/10.21817/ijestopenalex-snapshot
International Journal of Engineering Science and Technology
inferredcites →https://doi.org/10.2307/2411724openalex-snapshot
Distance as a Measure of Taxonomic Similarity
inferredcites →https://doi.org/10.3233/ida-2007-11402openalex-snapshot
In search of deterministic methods for initializing K-means and Gaussian mixture clustering
inferredcites →https://doi.org/10.3844/ajassp.2008.1247.1250openalex-snapshot
New Efficient Strategy to Accelerate k-Means Clustering Algorithm
inferredcites →https://doi.org/10.4156/jcit.vol7.issue5.38openalex-snapshot
An Improved Semi-supervised Clustering algorithm based on Initial Center Points
inferredcites →https://doi.org/10.4304/jcp.6.2.271-279openalex-snapshot
An Efficient Global K-means Clustering Algorithm
inferredcites →https://doi.org/10.5120/2999-4030openalex-snapshot
An Initialization Method for the Kmeans Algorithm using RNN and Coupling Degree
inferredcites →https://doi.org/10.5120/3573-4930openalex-snapshot
Initializing KMeans Clustering Algorithm using Statistical Information
inferredcites →https://doi.org/10.5120/ijca2018918234openalex-snapshot
Study and Implementing K-mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K
inferredcites →https://doi.org/10.5121/ijcnc.2011.3402openalex-snapshot
Fast K-Means Algorithm Clustering
inferredcites →https://doi.org/10.5281/zenodo.1079847openalex-snapshot
K-Means For Spherical Clusters With Large Variance In Sizes
inferredcites →https://doi.org/10.5281/zenodo.1334074openalex-snapshot
A New Algorithm For Cluster Initialization
inferredcites →https://doi.org/10.5555/1283383.1283494openalex-snapshot
k-means++: the advantages of careful seeding
inferredcites →https://doi.org/10.5753/jidm.2013.1482openalex-snapshot
Early Classification: A New Heuristic to Improve the Classification Step of K-Means
inferredcites →https://doi.org/10.5815/ijisa.2012.01.03openalex-snapshot
Efficient and Fast Initialization Algorithm for K-means Clustering
inferredcites →https://doi.org/10.5815/ijisa.2013.03.04openalex-snapshot
Efficient Data Clustering Algorithms: Improvements over Kmeans
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