Clustering: Difference between revisions

From Machinelearning
Line 13: Line 13:


Some divide clustering into two subgroups<ref>[https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ An Introduction to Clustering and different methods of clustering]analyticsvidhya.com</ref>:
Some divide clustering into two subgroups<ref>[https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ An Introduction to Clustering and different methods of clustering]analyticsvidhya.com</ref>:
* [[Hard clustering]]: Each data point either belongs to a cluster completely or not.  
* [[Hard clustering]]: Each data point either belongs to a cluster completely or not. Clusters do not overlap.
* [[Soft clustering]]: A probability or likelihood is assigned for putting data points into separate clusters.
* [[Soft clustering]]: A probability or likelihood is assigned for putting data points into separate clusters. Clusters may overlap.


== Clustering vs classification ==
== Clustering vs classification ==

Revision as of 14:38, 15 April 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]

Motivation

Generally, clustering can be used for one of the following purposes[1]:

Types pof clustering

Some divide clustering into two subgroups[2]:

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

Clustering vs classification

Algorithms

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


others

Applications

External links

References

References