Data mining in manufacturing: a review based on the kind of knowledge

By Alok Kumar Choudhary, J. Harding, Manoj Kumar Tiwari. Journal of Intelligent Manufacturing, 2009

519 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Information Systems, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1bf4c2dd6cd9b275c34972bf48fa5751b4ea637d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Hussain Ahmad Madni
2. Zahid Anwar
3. Munam Ali Shah

In Information Systems

1. Hussain Ahmad Madni
2. Zahid Anwar
3. Munam Ali Shah

In Statistics and Probability

1. Gülser Köksal
2. Murat Caner Testik
3. Inci Batmaz
4. Christian H. Weiβ
5. William H. Woodall

Citations in the corpus

• A review of data mining applications for quality improvement in manufacturing industry (2011, background): “Even though DM functions are typically classified as data summarisation, classification, prediction, clustering and so on, these classes depend very much on the type of knowledge that can be discovered in databases (Choudhary et al., 2008; Wei, Piramuthu, & Shaw, 2003).”
• Data mining techniques and applications — A decade review (2017, background): “There are many advantages of data mining like it is helpful in banking, finance, accounting, retail, marketing, manufacturing, governments and many more [7].”