Clustering: Difference between revisions
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'''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.<ref name="MLPython">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Nlxjw/intro-to-clustering Intro to Clustering]Coursera</ref> | '''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.<ref name="MLPython">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Nlxjw/intro-to-clustering Intro to Clustering]Coursera</ref> Clustering is one of the most used applications of unsupervised learning. | ||
== Motivation == | == Motivation == | ||
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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. 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 == | ||
Latest revision as of 02:08, 12 May 2022
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] Clustering is one of the most used applications of unsupervised learning.
Motivation
Generally, clustering can be used for one of the following purposes[1]:
- Exploratory data analysis
- Summary generation
- Outlier detection[1]
- 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. 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
- ↑ 1.0 1.1 1.2 1.3 1.4 Intro to ClusteringCoursera
- ↑ An Introduction to Clustering and different methods of clusteringanalyticsvidhya.com
- ↑ Real World Applications of Unsupervised Learningpythonistaplanet.com