Fuzzy decision trees to help flexible querying

Christophe Marsala

Kybernetika (2000)

  • Volume: 36, Issue: 6, page [689]-705
  • ISSN: 0023-5954

Abstract

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Fuzzy data mining by means of the fuzzy decision tree method enables the construction of a set of fuzzy rules. Such a rule set can be associated with a database as a knowledge base that can be used to help answering frequent queries. In this paper, a study is done that enables us to show that classification by means of a fuzzy decision tree is equivalent to the generalized modus ponens. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation evolves.

How to cite

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Marsala, Christophe. "Fuzzy decision trees to help flexible querying." Kybernetika 36.6 (2000): [689]-705. <http://eudml.org/doc/33511>.

@article{Marsala2000,
abstract = {Fuzzy data mining by means of the fuzzy decision tree method enables the construction of a set of fuzzy rules. Such a rule set can be associated with a database as a knowledge base that can be used to help answering frequent queries. In this paper, a study is done that enables us to show that classification by means of a fuzzy decision tree is equivalent to the generalized modus ponens. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation evolves.},
author = {Marsala, Christophe},
journal = {Kybernetika},
keywords = {fuzzy data mining; fuzzy decision trees; fuzzy data mining; fuzzy decision trees},
language = {eng},
number = {6},
pages = {[689]-705},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Fuzzy decision trees to help flexible querying},
url = {http://eudml.org/doc/33511},
volume = {36},
year = {2000},
}

TY - JOUR
AU - Marsala, Christophe
TI - Fuzzy decision trees to help flexible querying
JO - Kybernetika
PY - 2000
PB - Institute of Information Theory and Automation AS CR
VL - 36
IS - 6
SP - [689]
EP - 705
AB - Fuzzy data mining by means of the fuzzy decision tree method enables the construction of a set of fuzzy rules. Such a rule set can be associated with a database as a knowledge base that can be used to help answering frequent queries. In this paper, a study is done that enables us to show that classification by means of a fuzzy decision tree is equivalent to the generalized modus ponens. Moreover, it is shown that the decision taken by means of a fuzzy decision tree is more stable when observation evolves.
LA - eng
KW - fuzzy data mining; fuzzy decision trees; fuzzy data mining; fuzzy decision trees
UR - http://eudml.org/doc/33511
ER -

References

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