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.<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> | 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 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</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 17:14, 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
- ↑ 7 Innovative Uses of Clustering Algorithms in the Real Worlddatafloq.com
- ↑ KMeansscikit-learn.org