[1]
E.W.T. Ngai, Li Xiu , D.C.K. Chau. Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications 36 (2009) 2592-2602.
DOI: 10.1016/j.eswa.2008.02.021
Google Scholar
[2]
P. Yang, S.S. Liu, Fault Diagnosis for boilers in thermal power plant by data mining, in: Proceedings of Eighth International Conference on Control, Automation, Robotics and Vision, Kunming, China, (2004) December 6–9.
DOI: 10.1109/icarcv.2004.1469502
Google Scholar
[3]
Kai-Ying Chen , Long-Sheng Chen, Mu-Chen Chen , Chia-Lung Lee. Using SVM based method for equipment fault detection in a thermal power plant, Computers in Industry 62 (2011) 42–50.
DOI: 10.1016/j.compind.2010.05.013
Google Scholar
[4]
Sankar Mahadevan, Sirish L. Shah . Fault detection and diagnosis in process data using one-class support vector machines, Journal of Process Control 19 (2009) 1627–1639.
DOI: 10.1016/j.jprocont.2009.07.011
Google Scholar
[5]
M.S. Choudhury, S. Shah, N. Thornhill, D.S. Shook, Automatic detection and quantification of stiction in control valves, Control Engineering Practice 14 (12) (2006) 1395–1412.
DOI: 10.1016/j.conengprac.2005.10.003
Google Scholar
[6]
Julien Rabatel , Sandra Bringay, Pascal Poncelet. Anomaly detection in monitoring sensor data for preventive maintenance, Expert Systems with Applications 38 (2011) 7003–7015.
DOI: 10.1016/j.eswa.2010.12.014
Google Scholar
[7]
Chi Y, Wang H, Yu PS, Muntz RR Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. Knowl Inf Syst (2006) 10(3): 265–294.
DOI: 10.1007/s10115-006-0003-0
Google Scholar
[8]
Uno T, Asai T, Uchida Y, Arimura H An efficient algorithm for enumerating frequent closed patterns in transaction databases. In: Proc. of the 7th international conference on discovery science. LNAI vol 3245, Springer, Heidelberg, (2004) p.16–30.
DOI: 10.1007/978-3-540-30214-8_2
Google Scholar