Instance representing a set or cluster of instances.

Allows the visualization of weighted relationships between several entities. The graph has a circular shape, where each entity is represented by a fragment on the outside of the circular arrangement. The size of each of these fragments reflects the frequency at which that entity occurs. The fragments of different entities are connected by bands or links where the width or size of the band corresponds to the frequency of occurrence of that combination of entities in the data set.

Name given to the task of making class or category label predictions for new samples.

Each of the different classification model types according to the type of discriminant function considered.

Cloud computing allows us to work with computing and storage servers on a network, usually over the internet.

A set of instances that resemble each other.

An approach used to analyze the RAs obtained. It consists of analyzing the rules by grouping them according to the items they contain. We can analyze them by creating groups of rules with some common element in the antecedent or consequent. Groups can also be created by selecting rules that have the same itemset in the consequent and antecedent, allowing the study of different rules with related associations.

These are unsupervised learning techniques whose objective is to group available objects (instances) so that objects within the same group (cluster) are like each other and are different from objects in other groups.

In regression it refers to the phenomenon by which one variable, X1, is a linear combination of another variable, X2.

A computer cluster is a (large) set of computers connected by a network which behave as if they were one single computer.