An Approach to Concept Formation Based on Formal Concept Analysis

Tu Bao HO  

IEICE TRANSACTIONS on Information and Systems   Vol.E78-D   No.5   pp.553-559
Publication Date: 1995/05/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Algorithmic Learning Theory)
Category: Machine Learning and Its Applications
machine learning,  concept formation,  formal concept analysis,  concept lattice,  concept hierarchy,  

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Computational approaches to concept formation often share a top-down, incremental, hill-climbing classification, and differ from each other in the concept representation and quality criteria. Each of them captures part of the rich variety of conceptual knowledge and many are well suited only when the object-attribute distribution is not sparse. Formal concept analysis is a set-theoretic model that mathematically formulates the human understanding of concepts, and investigates the algebraic structure, Galois lattice, of possible concepts in a given domain. Adopting the idea of representing concepts by mutual closed sets of objects and attributes as well as the Galois lattice structure for concepts from formal concept analysis, we propose an approach to concept formation and develop OSHAM, a method that forms concept hierarchies with high utility score, clear semantics and effective even with sparse object-attribute distributions. In this paper we describe OSHAM, and in an attempt to show its performance we present experimental studies on a number of data sets from the machine learning literature.