An extended process model of knowledge discovery in database

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An extended process model of knowledge discovery in database. / Li, Tianrui; Ruan, Da; Laes, Erik (Peer reviewer); Carlé, Benny (Peer reviewer).

In: Journal of Enterprise Information Managment, Vol. 20, No. 2, 02.2007, p. 169-177.

Research output: Contribution to journalArticlepeer-review

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Li, T, Ruan, D, Laes, E & Carlé, B 2007, 'An extended process model of knowledge discovery in database', Journal of Enterprise Information Managment, vol. 20, no. 2, pp. 169-177. https://doi.org/10.1108/17410390710725751

Vancouver

Author

Li, Tianrui ; Ruan, Da ; Laes, Erik ; Carlé, Benny. / An extended process model of knowledge discovery in database. In: Journal of Enterprise Information Managment. 2007 ; Vol. 20, No. 2. pp. 169-177.

Bibtex - Download

@article{b66eeb743bb14c76abdbdf1ff660fec5,
title = "An extended process model of knowledge discovery in database",
abstract = "Much research on knowledge discovery in database (KDD) merely pays attention to data mining, one of many interacting steps in the process of discovering previously unknown and potentially interesting patterns in large databases, but little to the whole process. However, such approaches cannot satisfy the need of real applications of KDD. A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad et al. A case study by a reduct method from rough set theory is to illustrate why the process model is proposed and in what situation it can be used in practice. This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications. This model reflects the native of KDD in some tested cases. It may need further research to be used in all other situations. It can be used in the area of information security, medical treatment and other information management. Using this model, one can directly collect data that are essential and useful for the mining results. It also offers practical help to those KDD researchers both from industry and academia.",
keywords = "Databases, Process planning",
author = "Tianrui Li and Da Ruan and Erik Laes and Benny Carl{\'e}",
note = "Score = 10",
year = "2007",
month = feb,
doi = "10.1108/17410390710725751",
language = "English",
volume = "20",
pages = "169--177",
journal = "Journal of Enterprise Information Management",
issn = "1741-0398",
publisher = "Emerald Group Publishing",
number = "2",

}

RIS - Download

TY - JOUR

T1 - An extended process model of knowledge discovery in database

AU - Li, Tianrui

AU - Ruan, Da

A2 - Laes, Erik

A2 - Carlé, Benny

N1 - Score = 10

PY - 2007/2

Y1 - 2007/2

N2 - Much research on knowledge discovery in database (KDD) merely pays attention to data mining, one of many interacting steps in the process of discovering previously unknown and potentially interesting patterns in large databases, but little to the whole process. However, such approaches cannot satisfy the need of real applications of KDD. A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad et al. A case study by a reduct method from rough set theory is to illustrate why the process model is proposed and in what situation it can be used in practice. This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications. This model reflects the native of KDD in some tested cases. It may need further research to be used in all other situations. It can be used in the area of information security, medical treatment and other information management. Using this model, one can directly collect data that are essential and useful for the mining results. It also offers practical help to those KDD researchers both from industry and academia.

AB - Much research on knowledge discovery in database (KDD) merely pays attention to data mining, one of many interacting steps in the process of discovering previously unknown and potentially interesting patterns in large databases, but little to the whole process. However, such approaches cannot satisfy the need of real applications of KDD. A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad et al. A case study by a reduct method from rough set theory is to illustrate why the process model is proposed and in what situation it can be used in practice. This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications. This model reflects the native of KDD in some tested cases. It may need further research to be used in all other situations. It can be used in the area of information security, medical treatment and other information management. Using this model, one can directly collect data that are essential and useful for the mining results. It also offers practical help to those KDD researchers both from industry and academia.

KW - Databases

KW - Process planning

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_77262

UR - http://knowledgecentre.sckcen.be/so2/bibref/4080

U2 - 10.1108/17410390710725751

DO - 10.1108/17410390710725751

M3 - Article

VL - 20

SP - 169

EP - 177

JO - Journal of Enterprise Information Management

JF - Journal of Enterprise Information Management

SN - 1741-0398

IS - 2

ER -

ID: 220538