An Extensive Empirical Comparison of <italic>k</italic>-means Initialization Algorithms

An Extensive Empirical Comparison of <italic>k</italic>-means Initialization Algorithms

The <italic>k</italic>-means clustering algorithm, whilst widely popular, is not without its drawbacks. In this paper, we focus on the sensitivity of <italic>k</italic>-means to its initial set of centroids. Since the cluster recovery performance of <italic>k</italic...

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Bibliographic Details
Main Authors: Simon Harris, Renato Cordeiro De Amorim
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means initialisation
clustering
Online Access:https://ieeexplore.ieee.org/document/9786801/

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https://ieeexplore.ieee.org/document/9786801/

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