Methods of Conceptual Clustering and their Relation to Numerical Taxonomy.
Abstract
Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statistical methods generally connoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward (symbolic as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our paper to the explication of conceptual clustering, and extension to the statistically based methods of numerical taxonomy. In conceptual clustering he formation of object clusters is dependent on the quality of 'higher-level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept representations might enhance the effectiveness of future conceptual clustering systems. Keywords: Conceptual clustering; Concept formation; Hierarchical classification; Numerical taxonomy; Heuristic search; Exploratory data analysis.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 22, 1985
- Accession Number
- ADA160221
Entities
People
- D. Fisher
- P. Langley
Organizations
- University of California, Irvine