Clustering: Difference between revisions

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'''Clustering''' is an [[unsupervised learning]] technique. It is used for grouping data points, or objects that are somehow similar. Clustering means finding clusters in a dataset, unsupervised.<ref>[https://www.coursera.org/learn/machine-learning-with-python/lecture/Nlxjw/intro-to-clustering Intro to Clustering]Coursera</ref>
'''Clustering''' is an [[unsupervised learning]] technique. It is used for grouping data points, or objects that are somehow similar. Clustering means finding clusters in a dataset, unsupervised.<ref name="MLPython">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Nlxjw/intro-to-clustering Intro to Clustering]Coursera</ref>


== Types pof clustering ==
== Types pof clustering ==
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* [[Expectation maximization]]:
* [[Expectation maximization]]:
* [[Hierarchical cluster analysis]] (HCA):
* [[Hierarchical cluster analysis]] (HCA):
* [[Partitioned-baseed clustering]]:
* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:
* [[Hierarchical clustering]]:
* [[Hierarchical clustering]]<ref name="MLPython"/>:
* [[Density-based clustering]]:
* [[Density-based clustering]]<ref name="MLPython"/>:


== Applications ==
== Applications ==

Revision as of 22:01, 31 March 2020

Clustering is an unsupervised learning technique. It is used for grouping data points, or objects that are somehow similar. Clustering means finding clusters in a dataset, unsupervised.[1]

Types pof clustering

Some divide clustering into two subgroups[2]:

  • Hard clustering: Each data point either belongs to a cluster completely or not.
  • Soft clustering: A probability or likelihood is assigned for putting data points into separate clusters.

Clustering vs classification

Algorithms

Some of the commonly used clustering algorithms are[3]:

Applications

References

References