Clustering Algorithms
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
How Fast Is the k-Means Method?
Algorithmica
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Least squares quantization in PCM
IEEE Transactions on Information Theory
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Hartigan's method for k-means clustering holds several potential advantages compared to the classical and prevalent optimization heuristic known as Lloyd's algorithm. E.g., it was recently shown that the set of local minima of Hartigan's algorithm is a subset of those of Lloyd's method. We develop a closed-form expression that allows to establish Hartigan's method for k-means clustering with any Bregman divergence, and further strengthen the case of preferring Hartigan's algorithm over Lloyd's algorithm. Specifically, we characterize a range of problems with various noise levels of the inputs, for which any random partition represents a local minimum for Lloyd's algorithm, while Hartigan's algorithm easily converges to the correct solution. Extensive experiments on synthetic and real-world data further support our theoretical analysis.