Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

Multivariate analysis. --- Cluster analysis.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

Multivariate analysis. --- Cluster analysis.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Image segmentation. --- Cluster analysis. --- Signal processing. --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication) --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.

Artificial intelligence. --- Cluster analysis. --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data.

barite --- mineralogy --- industrial application --- beneficiation --- specific gravity --- bauxite overburden --- Belterra Clay --- mineralogical quantification --- Rietveld analysis --- machine learning --- artificial intelligence --- mining --- mineralogical analysis --- bauxite --- available alumina --- reactive silica --- XRD --- PLSR --- lithium --- quantification --- clustering --- Rietveld --- cluster analysis --- spodumene --- petalite --- lepidolite --- triphylite --- zinnwaldite --- amblygonite --- chalcopyrite --- ore blending --- copper flotation --- nickel laterite --- ore sorting --- framboidal pyrite --- sulfide minerals --- flotation --- process mineralogy --- heavy minerals --- ilmenite --- titania slag --- rietveld --- Magneli phases --- n/a