Conceptual clustering is a machine learning paradigm for unsupervised classification developed mainly during the 1980s. It is distinguished from ordinary data clustering by generating a concept description for each generated class. Most conceptual clustering methods are capable of generating hierarchical category structures; see Categorization for more information on hierarchy. Conceptual clustering is closely related to formal concept analysis, decision tree learning, and mixture model learning.
Read more about Conceptual Clustering: Conceptual Clustering Vs. Data Clustering, List of Published Algorithms, Example: A Basic Conceptual Clustering Algorithm
Famous quotes containing the word conceptual:
“Our acceptance of an ontology is, I think, similar in principle to our acceptance of a scientific theory, say a system of physics; we adopt, at least insofar as we are reasonable, the simplest conceptual scheme into which the disordered fragments of raw experience can be fitted and arranged.”
—Willard Van Orman Quine (b. 1908)