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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/4729

Title: An Approach to Concept Formation Based on Formal Concept Analysis
Authors: Ho, Tu Bao
Keywords: machine learning
concept formation
formal concept analysis
concept lattice
concept hierarchy
Issue Date: 1995-05-20
Publisher: 電子情報通信学会
Magazine name: IEICE TRANSACTIONS on Information and Systems
Volume: E78-D
Number: 5
Start page: 553
End page: 559
Abstract: 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.
Rights: Copyright (C)1995 IEICE. Tu Bao HO, IEICE TRANSACTIONS on Information and Systems, E78-D(5), 1995, 553-559. http://www.ieice.org/jpn/trans_online/
URI: http://hdl.handle.net/10119/4729
Material Type: publisher
Appears in Collections:a10-1. 雑誌掲載論文 (Journal Articles)

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