Clustering: Difference between revisions
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* [[K-means clustering|K-means]]: | * [[K-means clustering|K-means]]: | ||
* [[Expectation maximization]]: | * [[Expectation maximization]]: | ||
* [[Hierarchical cluster analysis]] (HCA): | * [[Hierarchical cluster analysis]] (HCA): | ||
* [[Partitioned-baseed clustering]]: | |||
* [[Hierarchical clustering]]: | |||
* [[Density-based clustering]]: | |||
== Applications == | == Applications == | ||
Revision as of 21:59, 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]:
- K-means:
- Expectation maximization:
- Hierarchical cluster analysis (HCA):
- Partitioned-baseed clustering:
- Hierarchical clustering:
- Density-based clustering:
Applications
References
References
- ↑ Intro to ClusteringCoursera
- ↑ An Introduction to Clustering and different methods of clusteringanalyticsvidhya.com
- ↑ Real World Applications of Unsupervised Learningpythonistaplanet.com