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

Which of the following definitions best describes 'partitioning objective'?

A. A the concept (sometimes called unsupervised classification) is a data transformation objective where the aim is to organize input data (typically vectors of attributes) into classes, where the number of classes and their specifications are not known a priori. Depending on usage, the class assignment can be definite or probabilistic.
B. A the concept (sometimes called supervised classification) is a data transformation objective where the aim is to create a predictor from training data through a machine learning technique. The training data consist of pairs of objects (typically vectors of attributes) and
class labels for these objects. The resulting predictor can be used to attach class labels to any valid novel input object. Depending on usage, the prediction can be definite or probabilistic. A classification is learned from the training data and can then be tested on test data.
C. A the concept is a data transformation objective where the aim is to generate a collection of disjoint non-empty subsets whose union equals a non-empty input set.
D. A the concept is a data transformation objective in which the aim is to partition a sample of data into subsets such that the analysis is initially performed on a single subset, while the other subset(s) are retained for subsequent use in confirming and validating the initial analysis.

Answer: A the concept is a data transformation objective where the aim is to generate a collection of disjoint non-empty subsets whose union equals a non-empty input set.