InforLorV4, Hal, Checkpoint, bibRecord, 002F97

Association Rules and Statistics

Identifieur interne : 002F97 ( Hal/Checkpoint ); précédent : 002F96; suivant : 002F98

Association Rules and Statistics

Auteurs : Martine Cadot [France] ; Jean-Baptiste Maj [France] ; Tarek Ziadé

Source :

RBID : Hal:hal-00578418

Abstract

A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, which fits the goal of the manager, is the extraction of association rules (Hand, Mannila & Smyth, 2001). This extraction is a part of attribute-oriented induction (Guyon & Elisseeff, 2003). The aim of this paper is to compare both types of extracted knowledge: association rules and results of statistics.

Url:

https://hal.archives-ouvertes.fr/hal-00578418

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Hal:hal-00578418

Le document en format XML

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<front><div type="abstract" xml:lang="en">A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, which fits the goal of the manager, is the extraction of association rules (Hand, Mannila & Smyth, 2001). This extraction is a part of attribute-oriented induction (Guyon & Elisseeff, 2003). The aim of this paper is to compare both types of extracted knowledge: association rules and results of statistics.</div>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Association Rules and Statistics</title>
<author role="crp"><persName><forename type="first">Martine</forename>
<surname>Cadot</surname>
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<email>cadot@loria.fr</email>
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<author role="aut"><persName><forename type="first">Jean-Baptiste</forename>
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<email>JBMaj@Loria.fr</email>
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<affiliation ref="#struct-2359"></affiliation>
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<author role="aut"><persName><forename type="first">Tarek</forename>
<surname>Ziadé</surname>
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<email>tarek@ziade.org</email>
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<monogr><title level="m">Encyclopedia of Data Warehousing and Mining</title>
<editor>John Wang</editor>
<imprint><publisher>Information Science Reference</publisher>
<biblScope unit="pp">94-97</biblScope>
<date type="datePub">2009</date>
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<abstract xml:lang="en">A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, which fits the goal of the manager, is the extraction of association rules (Hand, Mannila & Smyth, 2001). This extraction is a part of attribute-oriented induction (Guyon & Elisseeff, 2003). The aim of this paper is to compare both types of extracted knowledge: association rules and results of statistics.</abstract>
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Association Rules and Statistics

Association Rules and Statistics

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