K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics Share and Cite: Ye, S. (2025) Application of the K-Means Algorithm in the Study of Influenza Transmission Patterns.

Because of this, k-means clustering can yield different results on different runs of the algorithm — which isn’t ideal in mission-critical domains like finance.

The k-means algorithm is applicable only for purely numeric data. Data clustering is used as part of several machine-learning algorithms, and data clustering can also be used to perform ad hoc data ...

A k-means-type algorithm is proposed for efficiently clustering data constrained to lie on the surface of a p-dimensional unit sphere, or data that are mean-zero-unit-variance standardized ...

Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data ...

When using the k-means clustering algorithm, and in fact almost all clustering algorithms, the number of clusters, k, must be specified. Like many R functions, kmeans has a large number of optional ...

Based on the full analysis of the advantages and disadvantages of the traditional K - means and BIRCH clustering algorithms, an improved incremental clustering algorithm based on the core tree is ...

This article demonstrates K-means clustering benchmarking as a case study for Spark resource allocation and tuning analysis. Spark K-Means resource tuning: Introduction to K-means clustering K-Means ...