Conceptual Clustering

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:

    We must not leap to the fatalistic conclusion that we are stuck with the conceptual scheme that we grew up in. We can change it, bit by bit, plank by plank, though meanwhile there is nothing to carry us along but the evolving conceptual scheme itself. The philosopher’s task was well compared by Neurath to that of a mariner who must rebuild his ship on the open sea.
    Willard Van Orman Quine (b. 1908)