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https://hdl.handle.net/2142/19061
Description
Title
Symbolic clustering
Author(s)
Reinke, Robert Eugene
Issue Date
1991
Doctoral Committee Chair(s)
Baskin, Arthur B., III
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Computer Science
Language
eng
Abstract
Clustering is the problem of finding a good organization for data. Because there are many kinds of clustering problems, and because there are many possible clusterings for any data set, clustering programs use knowledge and assumptions about individual problems to make clustering tractable. Cluster analysis techniques allow knowledge to be expressed in the choice of a pairwise distance measure and in the choice of clustering algorithm. Conceptual clustering adds knowledge and preferences about cluster descriptions. In this dissertation, I describe symbolic clustering, which adds representation choice to the set of ways a data analyst can use problem-specific knowledge. I will develop an informal model for symbolic clustering, and use it to suggest where and how knowledge can be expressed in clustering. A language for creating symbolic clusterers, based on the model, has been developed and tested on three real clustering problems. The dissertation concludes with a discussion of the implications of the model and the results for clustering in general.
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