Hard clustering: Difference between revisions
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Hard clustering is a type of clustering which consists in grouping the data items such that each item is only assigned to one cluster.<ref name="sd">[https://medium.com/fintechexplained/machine-learning-hard-vs-soft-clustering-dc92710936af Machine Learning Hard Vs Soft Clustering]medium.com</ref> | Hard clustering is a type of [[clustering]] which consists in grouping the data items such that each item is only assigned to one cluster.<ref name="sd">[https://medium.com/fintechexplained/machine-learning-hard-vs-soft-clustering-dc92710936af Machine Learning Hard Vs Soft Clustering]medium.com</ref> | ||
== Algorithms == | == Algorithms == | ||
Some hard clustering algorithms are: | Some hard clustering algorithms are: | ||
Latest revision as of 14:34, 15 April 2020
Hard clustering is a type of clustering which consists in grouping the data items such that each item is only assigned to one cluster.[1]
Algorithms
Some hard clustering algorithms are:
- K-means: A famous hard clustering algorithm whereby the data items are clustered into K clusters such that each item only blogs to one cluster.[1]
References
- ↑ 1.0 1.1 Machine Learning Hard Vs Soft Clusteringmedium.com