K-means clustering - Revision history

Sebastian at 21:10, 31 March 2020

2020-03-31T21:10:35Z

← Older revision		Revision as of 21:10, 31 March 2020
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	The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters. It aims to minimize the “intra cluster” distances and maximize the “inter-cluster” distances, and to divide the data into non-overlapping clusters without any cluster-internal structure.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>		The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative [[unsupervised learning]] process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters. It aims to minimize the “intra cluster” distances and maximize the “inter-cluster” distances, and to divide the data into non-overlapping clusters without any cluster-internal structure.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

	== See also ==		== See also ==

Sebastian at 21:08, 31 March 2020

2020-03-31T21:08:16Z

← Older revision		Revision as of 21:08, 31 March 2020
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	The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>		The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters. It aims to minimize the “intra cluster” distances and maximize the “inter-cluster” distances, and to divide the data into non-overlapping clusters without any cluster-internal structure.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

	== See also ==		== See also ==

Sebastian at 21:07, 31 March 2020

2020-03-31T21:07:06Z

← Older revision		Revision as of 21:07, 31 March 2020
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	The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>		The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

			== See also ==

			* [[Clustering]]
			* [[Cluster]]

	== References ==		== References ==

Sebastian at 21:05, 31 March 2020

2020-03-31T21:05:53Z

← Older revision		Revision as of 21:05, 31 March 2020
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	The K-~~Means~~ clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-~~Means~~ can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>		The '''K-means clustering''' algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels. The objective of k-means is to form clusters in such a way that similar samples go into a cluster, and dissimilar samples fall into different clusters.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

	== References ==		== References ==

Sebastian at 21:02, 31 March 2020

2020-03-31T21:02:49Z

← Older revision		Revision as of 21:02, 31 March 2020
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	The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-Means can group data only unsupervised based on the similarity of customers to each other.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>		The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-Means can group data only unsupervised based on the similarity of customers to each other. It is a type of partitioning [[clustering]], as it divides the data into K non-overlapping subsets or clusters without any cluster internal structure or labels.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

	== References ==		== References ==

Sebastian at 21:01, 31 March 2020

2020-03-31T21:01:20Z

← Older revision		Revision as of 21:01, 31 March 2020
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	The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref>		The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref> K-Means can group data only unsupervised based on the similarity of customers to each other.<ref name="coursera">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Ky5Wf/intro-to-k-means Intro to k-Means]Coursera</ref>

	== References ==		== References ==

Sebastian at 17:14, 31 March 2020

2020-03-31T17:14:31Z

← Older revision		Revision as of 17:14, 31 March 2020
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	The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref>		The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224 7 Innovative Uses of Clustering Algorithms in the Real World]datafloq.com</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref>

	== References ==		== References ==

Sebastian at 17:13, 31 March 2020

2020-03-31T17:13:40Z

← Older revision		Revision as of 17:13, 31 March 2020
Line 1:		Line 1:
	The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<~~cite~~>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</~~cite~~> The k-means algorithm is one of the fastest clustering algorithms available.<~~cite~~>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</~~cite~~>		The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<ref>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</ref> The k-means algorithm is one of the fastest clustering algorithms available.<ref>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</ref>

	== References ==		== References ==

Sebastian at 17:10, 31 March 2020

2020-03-31T17:10:56Z

← Older revision		Revision as of 17:10, 31 March 2020
Line 1:		Line 1:
	The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<cite>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</cite> The k-means algorithm is one of the fastest clustering algorithms available.<cite>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html]</cite>		The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<cite>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</cite> The k-means algorithm is one of the fastest clustering algorithms available.<cite>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html KMeans]scikit-learn.org</cite>

			== References ==

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The K-Means clustering algorithm, a commonly used clustering algorithm, is an iterative process used to minimize the distance of the data point from the average data point in the cluster.<cite>[https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]</cite> The k-means algorithm is one of the fastest clustering algorithms available.<cite>[https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html]</cite>