Conceptual clustering of structured objects: A goal-oriented approach

摘要

Conceptual clustering is concerned with problems of grouping observed entities into conceptually simple classes. Earlier work on this subject assumed that the entities and classes are described in terms of a priori given multi-valued attributes. This research extends the previous work in three major ways: •- entities are characterized as compound objects requiring structural descriptions.•- relevant descriptive concepts (attributes and relations) are not necessarily given a priori but can be determined through reasoning about the goals of classification.•- inference rules are used to derive useful high-level descriptive concepts from the initially provided low-level concepts.The created classes are described using Annotated Predicate Calculus (APC), which is a typed predicate calculus with additional operators. Relevant descriptive concepts appropriate for characterizing entities are determined by tracing links in a Goal Dependency Network (GDN) that represents relationships between goals, subgoals, and related attributes.An experiment comparing results from the program cluster/s that implements the classification generation process and results obtained from people indicates that the proposed method might offer a plausible cognitive model of classification processes as well as an engineering solution to the problems of automatic classification generation.