TY - CHAP
T1 - Data Mining for Improving Manufacturing Processes.
AU - Rokach, Lior
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2009
Y1 - 2009
N2 - In many modern manufacturing plants, data that characterize the manufacturing process are electronically collected and stored in the organization’s databases. Thus, data mining tools can be used for automatically discovering interesting and useful patterns in the manufacturing processes. These patterns can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, reducing flow-time, increasing safety, etc. When data mining is directed towards improving manufacturing process, there are certain distinctions that should be noted compared to the classical methods employed in quality engineering, such as the experimental design. In data mining the primary purpose of the targeted database is not data analysis; the volume of the collected data makes it impractical to explore it using standard statistical procedures (Braha and Shmilovici, 2003).
AB - In many modern manufacturing plants, data that characterize the manufacturing process are electronically collected and stored in the organization’s databases. Thus, data mining tools can be used for automatically discovering interesting and useful patterns in the manufacturing processes. These patterns can be subsequently exploited to enhance the whole manufacturing process in such areas as defect prevention and detection, reducing flow-time, increasing safety, etc. When data mining is directed towards improving manufacturing process, there are certain distinctions that should be noted compared to the classical methods employed in quality engineering, such as the experimental design. In data mining the primary purpose of the targeted database is not data analysis; the volume of the collected data makes it impractical to explore it using standard statistical procedures (Braha and Shmilovici, 2003).
U2 - 10.4018/978-1-60566-010-3.ch066
DO - 10.4018/978-1-60566-010-3.ch066
M3 - Entry for encyclopedia/dictionary
SN - 9781605660103
SP - 417
EP - 423
BT - Encyclopedia of Data Warehousing and Mining
ER -