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231224s1985 xx ||||| 00| ||eng c |
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|a (NLM)21869296
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Cheng, Y
|e verfasserin
|4 aut
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| 245 |
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|a Conceptual clustering in knowledge organization
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| 264 |
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|c 1985
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 02.10.2012
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|a Date Revised 12.11.2019
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Knowledge organization is a very important step in building an expert system. The problem is how to organize knowledge into a conceptual structure and thus make it complete, concise, and consistent. In this paper, concepts used in knowledge description are divided into tangible ones and intermediate ones depending on whether or not they appear in the input or the output of the system. Intermediate concepts and their relationships with tangible concepts are subjected to changes. A distance measure for rules and an algorithm for conceptual clustering are described. New intermediate concepts are generated using this algorithm. A few new concepts may replace a large number of old relationships and also generate new rules for the system. An experiment on traditional Chinese medicine shows that the proposed method produces results similar to those generated by experts
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| 650 |
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|a Journal Article
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| 700 |
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|a Fu, K S
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 7(1985), 5 vom: 01. Mai, Seite 592-8
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g year:1985
|g number:5
|g day:01
|g month:05
|g pages:592-8
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