Semantic classification utilizes structural and semantical properties of data rather than purely their numerical values for constructing classes of objects. In the process of semantic interpretation of data sets, we arrive in our project EXPLORA at a collection of possible descriptions of a given goal set. We propose here a procedure for selecting certain classes from this collection. The procedure chooses them by means of their quality and of a kind of similarity, usually unsymmetric, which we call affinity. The idea is to suppress a class if it is sufficiently similar to, but also inferior to an other class that is itself retained. Some examples illustrate the method and its effect on the results.

Selection from Overlapping Classifications

A Support System for Interpreting Statistical Data.

Although it has a relatively short history, conceptual clustering is an especially active area of research in machine learning. There are a variety of ways in which conceptual patterns (the AI contribution to clustering) play a role in the clustering process. Two distinct conceptual clustering paradigms (conceptual sorting of exemplars and concept discovery) are described briefly. Then six types of conceptual clustering algorithms are characterized, attempting to cover the present spectrum of mechanisms used to conceptualize the clustering process.

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Concepts in conceptual clustering

In a learning situation with noisy data, a generalization procedure may find a sizeable collection of partial generalizations each of them covering a part of the positive examples and, in general, a few negative examples. The task is to choose a moderately sized sub-collection of partial generalizations that are sufficiently different from one another. The idea is that the user is less interested in all the “best” partial generalizations (according to a given criterion) if they are quite similar rather than in some diverse ones, even if they are, taken individually, less satisfactory. This vague goal concept is made more precise and a procedure is developed and examined that employs two notions: a measure for the quality of a single generalization (called evidence) and an asymmetric measure for the similarity of two generalizations (called affinity). These two co-operate in suppressing generalizations that are worse than, but not too different from, another generalization. The selection procedure works satisfactorily in different environments.

Selection from Overlapping Classifications

References

A Support System for Interpreting Statistical Data.

Concepts in conceptual clustering

Choosing among competing generalizations

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