Clustering - Revision history

Sebastian at 02:08, 12 May 2022

2022-05-12T02:08:51Z

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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 ==

2020-04-15T14:38:36Z

Types pof clustering

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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 ==

2020-04-14T16:27:24Z

Motivation

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	Generally, clustering can be used for one of the following purposes<ref name="MLPython"/>:		Generally, clustering can be used for one of the following purposes<ref name="MLPython"/>:
	* Exploratory data analysis		* [[Exploratory data analysis]]
	* Summary generation		* Summary generation
	* Outlier detection		* [[Outlier detection]][https://www.youtube.com/watch?v=hGKY6BAqJ6o]
	* Finding duplicates		* Finding duplicates
	* Pre-processing step		* Pre-processing step

2020-04-14T16:22:01Z

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	== Applications ==		== Applications ==

	== External ~~resources~~ ==		== External links ==

	* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[wikipedia:Pavel A. Pevzner\|Pavel A. Pevzner]]		* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[wikipedia:Pavel A. Pevzner\|Pavel A. Pevzner]]

2020-04-14T16:14:39Z

External resources

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	== External resources ==		== External resources ==

	* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[w:Pavel A. Pevzner\|Pavel A. Pevzner]]		* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[wikipedia:Pavel A. Pevzner\|Pavel A. Pevzner]]

	== References ==		== References ==

2020-04-14T16:14:29Z

External resources

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	== External resources ==		== External resources ==

	* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] {{w\|Pavel A. Pevzner}}		* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[w:Pavel A. Pevzner\|Pavel A. Pevzner]]

	== References ==		== References ==

2020-04-14T16:13:56Z

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	== Applications ==		== Applications ==

			== External resources ==

			* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] {{w\|Pavel A. Pevzner}}

	== References ==		== References ==

2020-04-14T14:55:34Z

← Older revision		Revision as of 14:55, 14 April 2020
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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 ==

2020-03-31T22:25:13Z

← Older revision		Revision as of 22:25, 31 March 2020
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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>

			== Motivation ==

			Generally, clustering can be used for one of the following purposes<ref name="MLPython"/>:
			* Exploratory data analysis
			* Summary generation
			* Outlier detection
			* Finding duplicates
			* Pre-processing step

	== Types pof clustering ==		== Types pof clustering ==

2020-03-31T22:05:44Z

Algorithms

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	Some of the commonly used clustering algorithms are<ref name="pythonistaplanet">[https://pythonistaplanet.com/applications-of-unsupervised-learning/ Real World Applications of Unsupervised Learning]pythonistaplanet.com</ref>:		Some of the commonly used clustering algorithms are<ref name="pythonistaplanet">[https://pythonistaplanet.com/applications-of-unsupervised-learning/ Real World Applications of Unsupervised Learning]pythonistaplanet.com</ref>:

	* [[K-means clustering\|K-means]]:
	* [[Expectation maximization]]:
	* [[Hierarchical cluster analysis]] (HCA):
	* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:		* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:
			** [[K-means clustering\|K-means]]:
			** [[K-median]]:
			** [[Fuzzy c-means]]:
	* [[Hierarchical clustering]]<ref name="MLPython"/>:		* [[Hierarchical clustering]]<ref name="MLPython"/>:
			** [[Agglomerative clustering]]:
			** [[Divisive clustering]]:
	* [[Density-based clustering]]<ref name="MLPython"/>:		* [[Density-based clustering]]<ref name="MLPython"/>:
			** [[DBSCAN]]:


			=== others ===

			* [[Expectation maximization]]:
			* [[Hierarchical cluster analysis]] (HCA):

	== Applications ==		== Applications ==