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[FL95] Faloutsos, C.; Lin, K.-I., FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets, In: Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’95) San Jose, CA. (May 1995), pp. 163–174.

[Fle87] Fletcher, R., Practical Methods of Optimization. (1987) John Wiley & Sons .

[FMMT96] Fukuda, T.; Morimoto, Y.; Morishita, S.; Tokuyama, T., Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization, In: Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’96) Montreal, Quebec, Canada. (June 1996), pp. 13–23.

[FP05]

[FPP07] Freedman, D.; Pisani, R.; Purves, R., Statistics. 4th ed. (2007) W. W. Norton & Co. .

[FPSS+96] In: (Editors: Fayyad, U.M.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R.) Advances in Knowledge Discovery and Data Mining (1996) AAAI/MIT Press.

[FP97] Fawcett, T.; Provost, F., Adaptive fraud detection, Data Mining and Knowledge Discovery 1 (1997) 291–316.

[FR02] Fraley, C.; Raftery, A.E., Model-based clustering, discriminant analysis, and density estimation, J. American Statistical Association 97 (2002) 611–631.

[Fri77] Friedman, J.H., A recursive partitioning decision rule for nonparametric classifiers, IEEE Trans. Computer 26 (1977) 404–408.

[Fri01] Friedman, J.H., Greedy function approximation: A gradient boosting machine, Ann. Statistics 29 (2001) 1189–1232.

[Fri03]

[FRM94] Faloutsos, C.; Ranganathan, M.; Manolopoulos, Y., Fast subsequence matching in time-series databases, In: Proc. 1994 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’94 Minneapolis, MN. (May 1994), pp. 419–429.

[FS93] Fayyad, U.; Smyth, P., Image database exploration: Progress and challenges, In: Proc. AAAI’93 Workshop Knowledge Discovery in Databases (KDD’93) Washington, DC. (July 1993), pp. 14–27.

[FS97] Freund, Y.; Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting, J. Computer and System Sciences 55 (1997) 119–139.

[FS06] Feldman, R.; Sanger, J., The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. (2006) Cambridge University Press .

[FSGM+98] Fang, M.; Shivakumar, N.; Garcia-Molina, H.; Motwani, R.; Ullman, J.D., Computing iceberg queries efficiently, In: Proc. 1998 Int. Conf. Very Large Data Bases (VLDB’98) New York, NY. (Aug. 1998), pp. 299–310.

[FW94] Furnkranz, J.; Widmer, G., Incremental reduced error pruning, In: Proc. 1994 Int. Conf. Machine Learning (ICML’94) New Brunswick, NJ. (1994), pp. 70–77.

[FWFY10] Fung, B.C.M.; Wang, K.; Fu, A.W.-C.; Yu, P.S., Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques. (2010) Chapman & Hall/CRC .

[FYM05] Fujimaki, R.; Yairi, T.; Machida, K., An approach to spacecraft anomaly detection problem using kernel feature space, In: Proc. 2005 Int. Workshop Link Discovery (LinkKDD’05) Chicago, IL. (2005), pp. 401–410.

[Gal93] Gallant, S.I., Neural Network Learning and Expert Systems. (1993) MIT Press, Cambridge, MA .

[Gat00] Gates, B., Business @ the Speed of Thought: Succeeding in the Digital Economy. (2000) Warner Books .

[GCB+97] Gray, J.; Chaudhuri, S.; Bosworth, A.; Layman, A.; Reichart, D.; Venkatrao, M.; Pellow, F.; Pirahesh, H., Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals, Data Mining and Knowledge Discovery 1 (1997) 29–54.

[GFKT01] Getoor, L.; Friedman, N.; Koller, D.; Taskar, B., Learning probabilistic models of relational structure, In: Proc. 2001 Int. Conf. Machine Learning (ICML’01) Williamstown, MA. (2001), pp. 170–177.

[GFS+01] Galhardas, H.; Florescu, D.; Shasha, D.; Simon, E.; Saita, C.-A., Declarative data cleaning: Language, model, and algorithms, In: Proc. 2001 Int. Conf. Very Large Data Bases (VLDB’01) Rome, Italy. (Sept. 2001), pp. 371–380.

[GG92] Gersho, A.; Gray, R.M., Vector Quantization and Signal Compression. (1992) Kluwer Academic .

[GG98] Gaede, V.; Günther, O., Multidimensional access methods, ACM Computing