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>[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 == | |||
Some hard clustering algorithms are: | |||
* [[K-means clustering|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.<ref name="sd"/> | |||
== References == | |||
Revision as of 15:06, 14 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