{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T18:03:18Z","timestamp":1754157798963,"version":"3.41.2"},"reference-count":16,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2007,2,20]],"date-time":"2007-02-20T00:00:00Z","timestamp":1171929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,2,20]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>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. The purpose of this work is to extend a process model of KDD in practice at large.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad<jats:italic>et al<\/jats:italic>. 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Research limitations\/implications<\/jats:title><jats:p>This model reflects the native of KDD in some tested cases. It may need further research to be used in all other situations.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>It can be used in the area of information security, medical treatment and other information management.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17410390710725751","type":"journal-article","created":{"date-parts":[[2007,2,28]],"date-time":"2007-02-28T11:19:38Z","timestamp":1172661578000},"page":"169-177","source":"Crossref","is-referenced-by-count":17,"title":["An extended process model of knowledge discovery in database"],"prefix":"10.1108","volume":"20","author":[{"given":"Tianrui","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2022021019575375900_b1","unstructured":"Brachman, R.J. and Anand, T. (1996), \u201cThe process of knowledge discovery in databases: a human\u2010centered approach\u201d, Advance in Knowledge Discovery and Data Mining, AAAI\/MIT Press, Menlo Park, CA\/Cambridge, MA, pp. 33\u201058."},{"key":"key2022021019575375900_b2","unstructured":"Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000), \u201cCRISP 1.0 process and user guide\u201d, CRISP\u2010DM Consortium, pp.1\u201015, available at: www.crisp\u2010dm.org."},{"key":"key2022021019575375900_b3","doi-asserted-by":"crossref","unstructured":"Fayyad, U., Piatetsky\u2010Shapiro, G. and Smyth, P. (1996), \u201cThe KDO process for extracting useful knowledge from volumes of data\u201d, Communications of the ACM, Vol. 39 No. 11, pp. 27\u201034.","DOI":"10.1145\/240455.240464"},{"key":"key2022021019575375900_b4","unstructured":"Gao, Y.R. (2000), \u201cData mining and its applications to engineering diagnosis\u201d, PhD thesis, Xi'an Jiaotong University, Xi'an."},{"key":"key2022021019575375900_b5","unstructured":"John, G.H. (1997), \u201cEnhancements to the data mining process\u201d, PhD thesis, Stanford University, Palo Alto, CA."},{"key":"key2022021019575375900_b6","doi-asserted-by":"crossref","unstructured":"Kemmerer, R.A. and Vigna, G. (2002), \u201cIntrusion detection: a brief history and overview\u201d, Computer, Vol. 35 No. 4, pp. 27\u201030.","DOI":"10.1109\/MC.2002.1012428"},{"key":"key2022021019575375900_b8","doi-asserted-by":"crossref","unstructured":"Li, T.R. and Xu, Y. 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(1996), \u201cAn introduction to intrusion detection\u201d, Crossroads: The ACM Student Magazine, Vol. 2 No. 4, pp. 3\u20107.","DOI":"10.1145\/332159.332161"},{"key":"key2022021019575375900_b14","unstructured":"Williams, G. and Huang, Z.H. (1996), \u201cModelling the KDD process\u201d, CSIRO DIT Data Mining Technical Report, TR\u2010DM\u201096013, available at: www.act.cmis.csiro.au\/edm\/papers\/kddmodel.pdf."},{"key":"key2022021019575375900_b15","unstructured":"Witten, I.H. and Frank, E. (2000), \u201cData mining: practical machine learning tools with Java implementations\u201d, Morgan Kaufmann, San Francisco, CA."},{"key":"key2022021019575375900_b16","unstructured":"Zhu, T.S., Gao, W., Ling, C.X., Gao, Z.Q. and Li, J.T. (1998), \u201cResearch on KDD process model\u201d, Proceedings of the Sixth China Workshop on Machine Learning, Beijing, available at: www.cs.ualberta.ca\/ \u223c\u2009tszhu\/paper\/CWML98.doc."},{"key":"key2022021019575375900_frg1","doi-asserted-by":"crossref","unstructured":"Li, T.R. and Ruan, D. 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