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Computational Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottazzi Schenone, Mariaelena +2 more
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2017
K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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K-Means is a very common method of unsupervised learning in data mining. It is introduced by Steinhaus in 1956. As time flies, many other enhanced methods of k-Means have been introduced and applied. One of the significant characteristic of k-Means is randomize.
Chen-Ling Tai, Chen-Shu Wang
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Data clustering: 50 years beyond K-means
Pattern Recognition Letters, 2008Anil K. Jain
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An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002T. Kanungo +5 more
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An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis
Pattern Recognition, 2023Haize Hu +3 more
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Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data mining and knowledge discovery, 1998J. Huang
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Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity
Neural computing & applications (Print), 2022R. Ghezelbash +4 more
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