Linear, deterministic, and order-invariant initialization methods for the k-means clustering algorithm
ME Celebi, HA Kingravi - Partitional clustering algorithms, 2015 - Springer
Over the past five decades, k-means has become the clustering algorithm of choice in many
application domains primarily due to its simplicity, time/space efficiency, and invariance to
the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection
of the cluster centers remains to be its most serious drawback. Numerous initialization
methods have been proposed to address this drawback. Many of these methods, however,
have time complexity superlinear in the number of data points, which makes them …
application domains primarily due to its simplicity, time/space efficiency, and invariance to
the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection
of the cluster centers remains to be its most serious drawback. Numerous initialization
methods have been proposed to address this drawback. Many of these methods, however,
have time complexity superlinear in the number of data points, which makes them …
Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
M Emre Celebi, HA Kingravi - arXiv e-prints, 2014 - ui.adsabs.harvard.edu
Over the past five decades, k-means has become the clustering algorithm of choice in many
application domains primarily due to its simplicity, time/space efficiency, and invariance to
the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection
of the cluster centers remains to be its most serious drawback. Numerous initialization
methods have been proposed to address this drawback. Many of these methods, however,
have time complexity superlinear in the number of data points, which makes them …
application domains primarily due to its simplicity, time/space efficiency, and invariance to
the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection
of the cluster centers remains to be its most serious drawback. Numerous initialization
methods have been proposed to address this drawback. Many of these methods, however,
have time complexity superlinear in the number of data points, which makes them …
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