Clustering algorithms are a powerful form of AI that can be applied to business challenges from customer segmentation to fraud detection.

K-Means Clustering An unsupervised learning algorithm, k-means clustering takes datasets with certain features and values related to these features and groups data points into a number of clusters.

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.

Clustering algorithms can be boiled down across many facets of the entire product range to create a smaller, more manageable set of components that form a data map.

Facility location and clustering algorithms constitute a critical area of research that bridges optimisation theory and data analysis. Facility location techniques focus on the strategic placement ...

Available clustering algorithms work only with structured data and use medoids as parameter for clustering.

Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets.

But clustering mixed categorical and numeric data is very tricky. This article presents a technique for clustering mixed categorical and numeric data using standard k-means clustering implemented ...