K-means clustering: Difference between revisions

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The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<cite>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</cite> The k-means algorithm is one of the fastest clustering algorithms available.<cite>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</cite>
The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref>


== References ==
== References ==

Revision as of 17:13, 31 March 2020

The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.[1] The k-means algorithm is one of the fastest clustering algorithms available.[2]

References

  1. ↑ [1]
  2. ↑ KMeansscikit-learn.org
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