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[GGR99] Ganti, V.; Gehrke, J.E.; Ramakrishnan, R., CACTUS—clustering categorical data using summaries, In: Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD’99) San Diego, CA. (1999), pp. 73–83.

[GGRL99] Gehrke, J.; Ganti, V.; Ramakrishnan, R.; Loh, W.-Y., BOAT—optimistic decision tree construction, In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99) Philadelphia, PA. (June 1999), pp. 169–180.

[GHL06] Gonzalez, H.; Han, J.; Li, X., Flowcube: Constructuing RFID flowcubes for multi-dimensional analysis of commodity flows, In: Proc. 2006 Int. Conf. Very Large Data Bases (VLDB’06) Seoul, Korea. (Sept. 2006), pp. 834–845.

[GHLK06] Gonzalez, H.; Han, J.; Li, X.; Klabjan, D., Warehousing and analysis of massive RFID data sets, In: Proc. 2006 Int. Conf. Data Engineering (ICDE’06) Atlanta, GA. (Apr. 2006), p. 83.

[GKK+01] Grossman, R.L.; Kamath, C.; Kegelmeyer, P.; Kumar, V.; Namburu, R.R., Data Mining for Scientific and Engineering Applications. (2001) Kluwer Academic .

[GKR98] Gibson, D.; Kleinberg, J.M.; Raghavan, P., Clustering categorical data: An approach based on dynamical systems, In: Proc. 1998 Int. Conf. Very Large Data Bases (VLDB’98) New York, NY. (Aug. 1998), pp. 311–323.

[GM99] Gupta, A.; Mumick, I.S., Materialized Views: Techniques, Implementations, and Applications. (1999) MIT Press, Cambridge, MA .

[GMMO00] Guha, S.; Mishra, N.; Motwani, R.; O’Callaghan, L., Clustering data streams, In: Proc. 2000 Symp. Foundations of Computer Science (FOCS’00) Redondo Beach, CA. (2000), pp. 359–366.

[GMP+09] Ginsberg, J.; Mohebbi, M.H.; Patel, R.S.; Brammer, L.; Smolinski, M.S.; Brilliant, L., Detecting influenza epidemics using search engine query data, Nature 457 (Feb. 2009) 1012–1014.

[GMUW08] Garcia-Molina, H.; Ullman, J.D.; Widom, J., Database Systems: The Complete Book. 2nd ed. (2008) Prentice Hall .

[GMV96] Guyon, I.; Matic, N.; Vapnik, V., Discoverying informative patterns and data cleaning, In: (Editors: Fayyad, U.M.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R.) Advances in Knowledge Discovery and Data Mining AAAI/MIT Press. (1996), pp. 181–203.

[Gol89] Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning. (1989) Addison-Wesley, Reading, MA .

[GR04] Grossman, D.A.; Frieder, O., Information Retrieval: Algorithms and Heuristics. (2004) Springer, New York .

[GR07] Grunwald, P.D.; Rissanen, J., The Minimum Description Length Principle. (2007) MIT Press, Cambridge, MA .

[GRG98] Gehrke, J.; Ramakrishnan, R.; Ganti, V., RainForest: A framework for fast decision tree construction of large datasets, In: Proc. 1998 Int. Conf. Very Large Data Bases (VLDB’98) New York, NY. (Aug. 1998), pp. 416–427.

[GRS98] Guha, S.; Rastogi, R.; Shim, K., CURE: An efficient clustering algorithm for large databases, In: Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98) Seattle, WA. (June 1998), pp. 73–84.

[GRS99] Guha, S.; Rastogi, R.; Shim, K., ROCK: A robust clustering algorithm for categorical attributes, In: Proc. 1999 Int. Conf. Data Engineering (ICDE’99) Sydney, Australia. (Mar. 1999), pp. 512–521.

[Gru69] Grubbs, F.E., Procedures for detecting outlying observations in samples, Technometrics 11 (1969) 1–21.

[Gup97] Gupta, H., Selection of views to materialize in a data warehouse, In: Proc. 7th Int. Conf. Database Theory (ICDT’97) Delphi, Greece. (Jan. 1997), pp. 98–112.

[Gut84] Guttman, A., R-Tree: A dynamic index structure for spatial searching, In: Proc. 1984 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’84) Boston, MA. (June 1984), pp. 47–57.

[GW07] Gonzalez, R.C.; Woods, R.E., Digital Image Processing. 3rd ed. (2007) Prentice Hall .

[GZ03a] Goethals, B.; Zaki, M., An introduction to workshop frequent itemset mining implementations, In: Proc. ICDM’03 Int. Workshop Frequent Itemset Mining Implementations (FIMI’03) Melbourne, FL. (Nov. 2003), pp. 1–13.

[GZ03b] Grahne, G.; Zhu, J., Efficiently using prefix-trees in mining frequent itemsets, In: Proc. ICDM’03 Int. Workshop on Frequent Itemset Mining Implementations (FIMI