Using case-based reasoning in interpreting unsupervised inductive learning results

Authors:
Tu Bao Ho;Chi Mai Luong
Affiliations:
School of Information Science, Japan Advanced Institute of Science and Technology, Tatsunokuchi, Ishikawa, Japan;Institute of Information Technology, National Center for Natural Science and Technology, Tuliem, Hanoi, Vietnam
Venue:
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Year:
1997

Citing 13
Cited 0

Instance-Based Learning Algorithms

Machine Learning
Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
An incremental concept formation approach for learning from databases

Theoretical Computer Science - Special issue on formal methods in databases and software engineering
Elements of machine learning

Elements of machine learning
A lattice conceptual clustering system and its application to browsing retrieval

Machine Learning
A hybrid model for concept formation

Information modelling and knowledge bases VII
Bayesian classification (AutoClass): theory and results

Advances in knowledge discovery and data mining
Discovering and using knowledge from unsupervised data

Decision Support Systems - Special issue: knowledge discovery and its applications to business decision making
Concept Formation and Knowledge Revision

Concept Formation and Knowledge Revision
Clustering Algorithms

Clustering Algorithms
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts concept hierarchies from unlabelled data, based on a representation combining the classical, prototype and exemplar views on concepts. The interpretive process is considered as an intrinsic part in OSHAM and is carried out by a combination of case-based reasoning with matching approaches in inductive learning. An experimental comparative study of some learning methods in terms of knowledge description and prediction is given.