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Clustering

Clustering

A process of unsupervised learning in which similar data points are identified and grouped together in order to help profile the attributes of different groups.

Clustering, or cluster analysis, is a process of unsupervised learning in which similar data points are identified and grouped together in order to help profile the attributes of different groups. The general aim of clustering is to maximize intra-cluster similarity while minimizing inter-cluster similarity. In other words, finding clusters such that the data points within a given cluster are as similar to each other as possible while the clusters themselves are as different from each other as possible.



There are several popular clustering algorithms used by data scientists depending on the specific applications involved. These popular algorithms include:

  • K-means
  • K-means++
  • Hierachical clustering
  • Density clustering
  • Spectral clustering
  • Consensus clustering
  • Expectation-maximization
  • Mean-shift

Applications of Clustering

Clustering is a prominent technique for data / statistical analysis and exploratory data mining, with applications in numerous fields. Some of the use cases for clustering include:



Timeline

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Further reading

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Author
Link
Type
Date

Clustering -- Unsupervised Learning

Anuja Nagpal

Web



Clustering in Machine Learning - GeeksforGeeks

Surya Priy

Web



The 5 Clustering Algorithms Data Scientists Need to Know

George Seif

Web



Documentaries, videos and podcasts

Title
Date
Link

Clustering Introduction - Practical Machine Learning Tutorial with Python p.34

June 7, 2016

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References