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Hierarchical classification as an aid to database and hit-list browsing
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Source Conference on Information and Knowledge Management archive
Proceedings of the third international conference on Information and knowledge management table of contents
Gaithersburg, Maryland, United States
Pages: 408 - 414  
Year of Publication: 1994
ISBN:0-89791-674-3
Authors
J. Royce Rose  Department of Computer Science, The University of South Carolina, Columbia, South Carolina
Johann Gasteiger  Computer-Chemie-Centrum, Universitaet Erlangen-Nuernberg, Naegelsbachstrasse 25, D-91052 Erlangen, Germany
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
NIST : National Institue of Standards & Technology
UMBC : U of MD Baltimore County
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

A navigational aid for databases that relies on unsupervised hierarchical classification is presented. The approach to hierarchical classification, based on both functional and topological features, supports the creation of deep hierarchies in which succeeding levels represent increasing degrees of abstraction. This allows the user to quickly evaluate the result of a query and to locate interesting items and classes of items by performing a tree traversal rather than a sequential perusal of a hit list or a series of ad hoc query refinements. In very large databases where classical querying methods are increasingly inadequate such as chemical reaction databases, such a browsing method is required in order to manage the flood of information with which the user is confronted.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
rose.abl:91
C.S. Ai, P. Blower, R. H. Ledwith. Extracting Reaction Information .from Chemical Databases. In Piatetsky-Shapiro, G., Frawley, W. J. (Eds.) Knowledge Discovery in Databases. AAAI Press/MIT Press Menlo Park, California, 1991.
 
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rose.pckw:89
K. Parsaye, M. Chignell, S. Khoshafiani,& H. Wong. Intelligent Databases, John Wiley & Sons, Inc., New York, 1989.
 
rose.ms:83
R.S. Michalski, R. Stepp. Learning from Observation: Conceptual Clustering. In R. S. Michalski, J. G. Carbonell & T. M. Mitchell (Eds.), Machine Learning: An artificial intelligence approach. Palo Alto, CA: Tioga Pubfishing Company, 1983.
 
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D. Fisher, P. Langley. Approaches to Conceptual Clustering. Proc. of the 9th Intl. Joint Conf. on Artificial Intelligence. Los Angeles, CA: Morgan Kaufman, 1985.
 
rose.fxz:92
 
rose.grc:90
 
rose.rg:89
J.R. Rose, H. Gelernter. ISOLDE: A System for Learning Organic Chemistry through Induction. In J.H. Boose, B.R. Gaines, & J.G. Ganascia (Eds.), EKAW-89: Third European Workshop on Knowledge Acquisition for Knowledge-Based Systems. Paris, France 1989.
 
rose.gm:80
J. Gasteiger, M. Marsili. Iterative Partial Equalization of Orbital Electronegativity- A Rapid Access to Atomic Charges. Tetrahedron, 1980, 36, 3219-3228.
 
rose.gs:85
J. Gasteiger, H. Sailer. Calculation of the Charge Distribution in Conjugated Systems by a Quantification of the Resonance Concept. Angew. Chem. 1985, 97, 699-701. Angew. Chem. Ed. Engl. 1985, 24, 687-689
 
rose.gsl:86
J. Gasteiger, H. Sailer, P. L6w. Elucidating Chemical Reactivity by Pattern Recognition Methods. Anal. Chim. Acta, 1986, 191, 111- 123.
 
rose.rg
J.R. Rose, H. Gelernter. Unsupervised Learning of Context.Sensitive Graph Rewritin9 Rules. To appear in Machine Learning.
 
rose.rg:94
J.R. Rose, :i. Gasteiger. HORACE: An Automatic System for the H#erarchical Classification of Chemical Reactions. J. Chem. Inf. Comput. Sci., 1994, 34, 74-90.
 
rose.bd:90


Collaborative Colleagues:
J. Royce Rose: colleagues
Johann Gasteiger: colleagues