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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/9795

Title: Data mining in manufacturing: a review based on the kind of knowledge
Authors: Choudhary, Alok K.
Harding, Jennifer A.
Tiwari, Manoj K.
Keywords: Knowledge discovery
Data mining
Manufacturing
Text mining
Literature review
Issue Date: 2009
Publisher: © Springer
Citation: CHOUDHARY, A.K., HARDING, J.A. and TIWARI, M.K., 2009. Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20 (5), pp. 501 - 521
Abstract: In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.
Description: This article was published in the serial, Journal of Intelligent Manufacturing [© Springer]. The definitive version is available at: http://dx.doi.org/10.1007/s10845-008-0145-x
Version: Accepted for publication
DOI: 10.1007/s10845-008-0145-x
URI: https://dspace.lboro.ac.uk/2134/9795
Publisher Link: http://dx.doi.org/10.1007/s10845-008-0145-x
ISSN: 0956-5515
Appears in Collections:Published Articles (Mechanical and Manufacturing Engineering)

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