Data Science for Business

Appendix C. Bibliography

Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1), 39–59. Available: http://www.iiia.csic.es/People/enric/AICom.html.

Adams, N. M., & Hand, D. J. (1999). Comparing classifiers when the misallocations costs are uncertain. Pattern Recognition, 32, 1139–1147.

Aha, D. W. (Ed.). (1997). Lazy learning. Kluwer Academic Publishers, Norwell, MA, USA.

Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6, 37–66.

Aggarwal, C., & Yu, P. (2008). Privacy-preserving Data Mining: Models and Algorithms. Springer, USA.

Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544-21549.

Arthur, D., & Vassilvitskii, S. (2007). K-means++: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035.

Attenberg, J., Ipeirotis, P., & Provost, F. (2011). Beat the machine: Challenging workers to find the unknown unknowns. In Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence.

Attenberg, J., & Provost, F. (2010). Why label when you can search?: Alternatives to active learning for applying human resources to build classification models under extreme class imbalance. In Proceedings of the ...