Clustering: Difference between revisions

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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.  
* 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.


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

Revision as of 14:55, 14 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]:

  • Exploratory data analysis
  • Summary generation
  • Outlier detection
  • Finding duplicates
  • Pre-processing step

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]:


others

Applications

References

References