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 of ...

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 ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

Entropy Minimization is a new clustering algorithm that works with both categorical and numeric data, and scales well to extremely large data sets. Data clustering is the process of placing data items ...

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

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 ...

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 ...

Clustering algorithms are used to generate clusters of elements having similar characteristics. Among the different groups of clustering algorithms, agglomerative algorithm is widely used in the ...

Data that resides in a fixed field within a record or file is called structured data and have a defined schema. Unstructured data refers to information that either does not have a pre-defined data ...