Using induction to mitigate cylinder banding during rotogravure printing is very much in the interactive induction framework -- human and machine do what each does best:

Evans, B., & Fisher, D. (1994). "Process delay analysis using decision tree induction," IEEE Expert, 9, 1, 60--66.

Evans, R., & Fisher, D. (2002). "Using decision tree induction to minimize process delays in the printing industry," in W. Klosgen and J. Zytkow (Eds.), Handbook of Data Mining and Knowledge Discovery. Oxford University Press, 874--881. preprint

Semi-supervised gene expression mining using rule-based systems (i.e., C4.5, Brute) is also an example of interactive induction, where human analyst, induction engine, and a curated knowledge base are the major components of the collective system:

Fisher, D., Edgerton, M., Chen, Z., Tang, L., & Frey, L. (2006). "Backward Chaining Rule Induction," Journal of Intelligent Data Analysis, 10, 5, 397--417.

Edgerton, M., Fisher, D., Tang, L., Frey, L., & Chen, Z. (2007). "Data mining for gene networks relevant to poor prognosis in lung cancer via backward-chaining rule induction: An implementation in lung cancer research," Cancer Informatics.