Clustering: Difference between revisions
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* 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 == | |||
== Algorithms == | == Algorithms == | ||
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* [[Expectation maximization]]: | * [[Expectation maximization]]: | ||
* [[Hierarchical cluster analysis]] (HCA): | * [[Hierarchical cluster analysis]] (HCA): | ||
== Applications == | |||
== References == | == References == | ||
Revision as of 21:55, 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
- ↑ Intro to ClusteringCoursera
- ↑ An Introduction to Clustering and different methods of clusteringanalyticsvidhya.com
- ↑ Real World Applications of Unsupervised Learningpythonistaplanet.com