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In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. [1]
Mining of sulfur from a deposit at the edge of Ijen's crater lake, Indonesia. Mining is the extraction of valuable geological materials and minerals from the surface of the Earth.
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Keeler et al., [2] in his work in the early 1990s was the first one to explore the area of MIL. The actual term multi-instance learning was introduced in the middle of the 1990s, by Dietterich et al. while they were investigating the problem of drug activity prediction. [3]
Relational data mining is the data mining technique for relational databases. [1] Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns).
Agent Mining is a research field that combines two areas of computer science: multiagent systems and data mining.It explores how intelligent computer agents can work together to discover, analyze, and learn from large amounts of data more effectively than traditional methods.
Crushing, a form of comminution, one of the unit operations of mineral processing. Mineral processing is the process of separating commercially valuable minerals from their ores in the field of extractive metallurgy. [1]