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Interactive data mining informs designed experiments
Author(s) -
Gaudard Marie,
Ramsey Philip,
Stephens Mia
Publication year - 2009
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.971
Subject(s) - construct (python library) , partition (number theory) , computer science , set (abstract data type) , data mining , data science , six sigma , data set , value (mathematics) , industrial engineering , operations research , artificial intelligence , machine learning , engineering , mathematics , operations management , programming language , combinatorics , lean manufacturing
We illustrate how a Six Sigma project team can apply recursive partitioning to a historical data set to narrow down a list of potential experimental factors and then construct an experimental design using information from the partition analysis. The paper illustrates the value of analyzing historical manufacturing data to inform the choice of factors and levels for statistically designed experiments. Copyright © 2008 John Wiley & Sons, Ltd.