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 ...

Co-clustering algorithms and models represent a robust framework for the simultaneous partitioning of the rows and columns in a data matrix. This dual clustering approach, often termed block ...

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 ...

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

Statistica Sinica, Vol. 12, No. 1, A Special Issue on Bioinformatics (January 2002), pp. 241-262 (22 pages) Many clustering algorithms have been used to analyze microarray gene expression data. Given ...

Data clustering is the process of placing data items into groups so that items within a group are similar and items in different groups are dissimilar. The most common technique for clustering numeric ...

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

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.

Semantic keyword clustering can take your keyword research to the next level. Here's a Python script to help you do just that.