Cluster analysis.

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

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

Cluster analysis and applications by: (Electronic resources)

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

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

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

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

Fuzzy models for pattern recognition : = methods that search for structures in data / by: (Language materials, printed)

Mixture models : = inference and applications to clustering / by: (Language materials, printed)

Cluster analysis / by: (Language materials, printed)

Foundations of quantization for probability distributions / by: (Language materials, printed)

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

Topics in modelling of clustered data / by: (Language materials, printed)

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

Cluster analysis for data mining and system identification / by: (Language materials, printed)

Fuzzy Models and Algorithms for Pattern Recognition and Image Processing by: (Language materials, printed)

Data Analysis and Decision Support by: (Language materials, printed)

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

Metaheuristic clustering|h[electronic resource] / by: (Language materials, printed)

Foundations of quantization for probability distributions by: (Language materials, printed)

Data clustering = theory, algorithms, and applications / by: (Electronic resources)

Clustering by: (Electronic resources)

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

Partitional clustering algorithms by: (Electronic resources)

Integrative cluster analysis in bioinformatics by: (Electronic resources)

Co-clustering = models, algoriths and applications / by: (Electronic resources)

Handbook of cluster analysis / by: (Language materials, printed)

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

Modern algorithms of cluster analysis by: (Electronic resources)

Data clustering = algorithms and applications / by: (Electronic resources)

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

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

Data analysis in bi-partial perspective = clustering and beyond / by: (Electronic resources)

Fuzzy collaborative forecasting and clustering = methodology, system architecture, and applications / by: (Electronic resources)

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

Evolutionary data clustering = algorithms and applications / by: (Electronic resources)

Clustering techniques for image segmentation by: (Electronic resources)

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