Constrained K-Means Clustering

By Kristin P. Bennett, Paul S. Bradley, Ayhan Demiriz. 2000

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ecadeb93378d7911c2f7b9bd83a8af55d7fa9e06

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Citations in the corpus

A Survey of Clustering Data Mining Techniques (2006, methodology): “The modification of the k-means objective function and of k-means updates that incorporate lower limits on cluster volumes is suggested in [37].”
A Unified Framework for Model-based Clustering (2003, background): “Fortunately, this integer programming problem is special in that it has the same optimum as its corresponding real relaxation (Bradley et al., 2000), which is a linear programming problem.”
Spectral Relaxation for K-means Clustering (2001, methodology): “Much research has been done on computing re ned initial points and adding explicit constraints to the sum-of-squares cost function for K-means clustering so that the search can converge to better local minimum [1, 2].”
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (2005, background): “This explicit choice simulates a frequency sensitive competitive learning that implicitly prevents the formation of null clusters, a well-known problem in regular kmeans (Bradley et al., 2000).”
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks (2019, methodology): “We utilize the constrained K-means algorithm [17] to perform the clustering process.”
Frequency-sensitive competitive learning for scalable balanced clustering on high-dimensional hyperspheres (2004, methodology): “Having shown the need for clustering high dimensional data residing on hyperspheres by drawing examples from document clustering and market basket analysis, we use the same two domains to motivate the desirability of obtaining balanced clusters, i.e., clusters of comparable sizes [18], [ 19 ].”
Balanced K-Means for Clustering (2014, methodology): “There is a convergence result in [6] (Proposition 2.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, unknown): “means algorithm [200] based on their physical locations.”