Data Mining in Chemical Process Industry

Y S Choudhary


Data mining is the computing process of learning patterns in huge data sets involving methods at the intersection of statistics, and database systems. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD. Data mining techniques are becoming increasingly important in chemistry as databases become very large to examine by hand. Data mining methods from the field of Inductive Logic Programming (ILP) have potential advantages for structural chemical data. Data mining was used to find all frequent substructures in the database, and knowledge of these frequent substructures is shown to add value to the database. Only by using a data mining algorithm, and by doing a complete search, is it possible to prove such a result. In this paper, study of data mining methods was presented which is the main novel tool for studying chemical databases.

Keywords: chemical databases, chemical industry, chemical modeling, data-based models,
data mining

Full Text:



M.D. Giess, S.J. Culley, A. Shepherd.

Informing Design Using Data Mining

Methods. ASME DETC, Montreal,

Canada, 2002, 98–106p.

M.D. Giess, S.J. Culley. Investigating

Manufacturing Data for Use Within

Design. ICED 03, Stockholm,

Sweden, 2003, 1408–13p.

M. Perzyk, A. Kochanski, J.

Kozlowsk. Data mining in

manufacturing: significance analysis

of process parameters, Proc IMechE

Part B: J Eng Manuf. 2008; 222:


Chen-Fu Chien, Wen-Chih Wang,

Jen-Chieh Cheng, Data mining for

yield enhancement in semiconductor

manufacturing and an empirical

study, Exp Syst Appl. 2007; 33: 192–


B. Al-Salim, M. Abdoli. Data mining

for decision support of the quality

improvement process, In:

Proceedings of the Eleventh

Americas Conference on Information

Systems. Omaha, NE, USA, August

th–14th 2000, 1462–9p.

S.-g. He, Z. He, G.A. Wang, L. Li.

In: Data Mining and Knowledge

Discovery in Real Life Application. J.

Ponce, A. Karahoca (eds.), February

, I-Tech, Vienna, Austria, 438p.

G. Koksa, I. Batmaz, M.C. Testik. A

review of data mining applications

for quality improvement in

manufacturing industry, Exp Syst

Appl. 2011; 38: 13448–67p.

J. Ranjan. Applications of data

mining techniques in pharmaceutical

industry, J Theor Appl Inform

Technol. 2005–2007; 61–7p.

M.A. Hussain. Data mining for

improving a cleaning process in the

semiconductor industry, IEEE Trans

Semicon Manuf. 2002; 15(1): 91–



  • There are currently no refbacks.