K-means clustering: Difference between revisions
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The K- | 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 == | |||
* [[Clustering]] | |||
* [[Cluster]] | |||
== References == | |||
Latest revision as of 21:10, 31 March 2020
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.[1] The k-means algorithm is one of the fastest clustering algorithms available.[2] 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.[3]
See also
References
- ↑ 7 Innovative Uses of Clustering Algorithms in the Real Worlddatafloq.com
- ↑ KMeansscikit-learn.org
- ↑ Intro to k-MeansCoursera