Cluster analysis.

Three-way scaling and clustering / by: (Language materials, printed)

Clustering for data mining :a data recovery approach / by: (Language materials, printed)

Cluster and classification techniques for the biosciences / by: (Language materials, printed)

Data clustering :theory, algorithms, and applications / by: (Language materials, printed)

Small business clustering technologiesapplications in marketing, management, IT and economics / by: (Electronic resources)

Cluster and classification techniques for the biosciences by: (Electronic resources)

Structure in complex networks by: (Electronic resources)

Metaheuristic clustering|h[electronic resource] / by: (Electronic resources)

Statistical methods for disease clustering by: (Electronic resources)

Foundations of quantization for probability distributions by: (Electronic resources)

Relational data clusteringmodels, algorithms, and applications / by: (Electronic resources)

Relational data clustering :models, algorithms, and applications / by: (Language materials, printed)

Clustering in bioinformatics and drug discovery / by: (Language materials, printed)

Cluster randomised trials / by: (Language materials, printed)

From curve fitting to machine learningan illustrative guide to scientific data analysis and computational intelligence / by: (Electronic resources)

Cluster analysis / by: (Language materials, printed)

Cluster randomised trials by: (Electronic resources)

Data clusteringtheory, algorithms, and applications / by: (Electronic resources)

Clustering by: (Electronic resources)

Data clustering in C++an object-oriented approach / by: (Electronic resources)

Clustering in bioinformatics and drug discovery by: (Electronic resources)

Clustering :a data recovery approach / by: (Language materials, printed)

Data clustering :algorithms and applications / by: (Language materials, printed)

Robust cluster analysis and variable selection / by: (Language materials, printed)

Partitional clustering algorithms by: (Electronic resources)

Integrative cluster analysis in bioinformatics by: (Electronic resources)

Co-clusteringmodels, algoriths and applications / by: (Electronic resources)

Modern algorithms of cluster analysis by: (Electronic resources)

Modern technologies for big data classification and clustering by: (Electronic resources)

Data clusteringalgorithms and applications / by: (Electronic resources)

Clustering methods for big data analyticstechniques, toolboxes and applications / by: (Electronic resources)

Intelligent multidimensional data clustering and analysis by: (Electronic resources)

Model-based clustering and classification for data science :with applications in R / by: (Language materials, printed)

Clustering methodology for symbolic data / by: (Language materials, printed)

Time series clustering and classification / by: (Language materials, printed)

Data analysis in bi-partial perspectiveclustering and beyond / by: (Electronic resources)

Fuzzy collaborative forecasting and clusteringmethodology, system architecture, and applications / by: (Electronic resources)

Nanotechnologies and clusters in the spaces of higher dimensionemerging research and opportunities / by: (Electronic resources)

An introduction to clustering with R by: (Electronic resources)

An introduction to clustering with R / by: (Language materials, printed)

Evolutionary data clusteringalgorithms and applications / by: (Electronic resources)

Cluster analysis and applications by: (Electronic resources)

Clustering techniques for image segmentation by: (Electronic resources)

Large-scale group decision-makingstate-to-the-art clustering and consensus paths / by: (Electronic resources)

Test data engineeringlatent rank analysis, biclustering, and Bayesian network / by: (Electronic resources)

Feature and dimensionality reduction for clustering with deep learning by: (Electronic resources)

Model-based clustering and classification for data sciencewith applications in R / by: (Electronic resources)