Task: Class Definition Understanding (ontology: Sciences/Computer_Science)

Which of the following definitions best describes 'clustering specification'?

A. A probability distribution D on type T specifies a mapping from objects of type T to non-negative Reals, i.e., has the signature d :: T → R0+. For uncountably infinite types, probability densities are used instead. The sum of all probabilities (the integral of the probability densities) over T is constrained to amount to one.
B. the concept is a single generalization specification is a generalization specification and denotes a type of generalization that models the mapping of a set of objects S of type T from S to a set of natural numbers {1,...K}. This generalization is obtained by appliing a clustering algorithm on a set of data.
C. A predictive model M for types Td, Tc specifies a function that takes an object of type Td and returns one of type Tc, i.e., has the signature m :: Td → Tc. Most often, predictive modelling is concerned with classification, where Tc would be Boolean (for binary classification) or Discrete(S) (for multi-class classification), or regression, where Tc would be Real. In our case, we allow both Td (description) and Tc (class/target) to be arbitrarily complex data types.
D. A patten specification is a single generalization specification and denotes a pattern P on type T which is a boolean funtion on objects of type T.

Answer: the concept is a single generalization specification is a generalization specification and denotes a type of generalization that models the mapping of a set of objects S of type T from S to a set of natural numbers {1,...K}. This generalization is obtained by appliing a clustering algorithm on a set of data.