ArticleStatistical analysis and data mining2018
The next-generation K-means algorithm.
Article in Statistical analysis and data mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
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- Differential Transcriptomic Landscapes of SARS-CoV-2 Variants in Multiple Organs from Infected Rhesus Macaques.Genomics, proteomics & bioinformatics · 2023Article
- scmNature communications · 2023Article
- The man, the plant, and the insect: shooting host specificity determinants inFrontiers in microbiology · 2023Article
- Differential transcriptomic landscapes of multiple organs from SARS-CoV-2 early infected rhesus macaques.Protein & cell · 2022Article
- Article
- The Distribution of Several Genomic Virulence Determinants Does Not Corroborate the Established Serotyping Classification ofInternational journal of molecular sciences · 2021Article
- A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.Journal of medical Internet research · 2021Article
- The next-generation K-means algorithm.Statistical analysis and data mining · 2018Article
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1 author.
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Abstract
Typically, when referring to a model-based classification, the mixture distribution approach is understood. In contrast, we revive the hard-classification model-based approach developed by Banfield and Raftery (1993) for which K-means is equivalent to the maximum likelihood (ML) estimation. The next-generation K-means algorithm does not end after the classification is achieved, but moves forward to answer the following fundamental questions: Are there clusters, how many clusters are there, what are the statistical properties of the estimated means and index sets, what is the distribution of the coefficients in the clusterwise regression, and how to classify multilevel data? The statistical model-based approach for the K-means algorithm is the key, because it allows statistical simulations and studying the properties of classification following the track of the classical statistics. This paper illustrates the application of the ML classification to testing the no-clusters hypothesis, to studying various methods for selection of the number of clusters using simulations, robust clustering using Laplace distribution, studying properties of the coefficients in clusterwise regression, and finally to multilevel data by marrying the variance components model with K-means.
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