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Clustering can be the assignment of a set of observations into subsets, where data in each subset have similarities with regard to one or more parameters while data in different subsets can be dissimilar with regard to one or more parameters. Clustering techniques can provide information on similarity or dissimilarity, for example reflected in a similarity metric, a measurement of internal compactness or separation between different clusters. A Bayesian network can be a graphical model representing, for example, random variables and their conditional independencies. This can be shown in a directed acyclic graph. A Bayesian network can represent the probabilistic relationships between diseases and symptoms. A Bayesian network can be used to compute the probability(ies) of the presence of one or more disease.

Added by DJM Jan 2024