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.
Conceptual clustering is obviously closely related to data clustering; however, in conceptual clustering it is not only the inherent structure of the data that drives cluster formation, but also the Description language which is available to the learner. Thus, a statistically strong grouping in the data may fail to be extracted by the learner if the prevailing concept description language is incapable of describing that particular regularity. In most implementations, the description language has been limited to feature conjunction, although in COBWEB (see "COBWEB" below), the feature language is probabilistic.
A fair number of algorithms have been proposed for conceptual clustering. Some examples are given below:
More general discussions and reviews of conceptual clustering can be found in the following publications:
This section discusses the rudiments of the conceptual clustering algorithm COBWEB. There are many other algorithms using different heuristics and "category goodness" or category evaluation criteria, but COBWEB is one of the best known. The reader is referred to the bibliography for other methods.
The COBWEB data structure is a hierarchy (tree) wherein each node represents a given concept. Each concept represents a set (actually, a multiset or bag) of objects, each object being represented as a binary-valued property list. The data associated with each tree node (i.e., concept) are the integer property counts for the objects in that concept. For example, (see figure), let a concept contain the following four objects (repeated objects being permitted).
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