Estimating the Mean Number of K-Means Clusters to Form
Robert A. Murphy
TL;DR
This paper introduces a method to estimate the average number of clusters produced by K-Means clustering based on the dataset's sample size using a random cluster model.
Contribution
It presents a novel approach to predict the mean number of clusters in K-Means using a theoretical model based on sample size.
Findings
Provides a formula for estimating the mean number of clusters
Validates the model with empirical data
Offers insights into cluster formation dynamics
Abstract
Utilizing the sample size of a dataset, the random cluster model is employed in order to derive an estimate of the mean number of K-Means clusters to form during classification of a dataset.
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Taxonomy
TopicsData Mining Algorithms and Applications · Advanced Clustering Algorithms Research · Machine Learning and Data Classification