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data for data mining applications, In: Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98) Seattle, WA. (June 1998), pp. 94–105.

[AGM04] Afrati, F.N.; Gionis, A.; Mannila, H., Approximating a collection of frequent sets, In: Proc. 2004 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’04) Seattle, WA. (Aug. 2004), pp. 12–19.

[AGS97] Agrawal, R.; Gupta, A.; Sarawagi, S., Modeling multidimensional databases, In: Proc. 1997 Int. Conf. Data Engineering (ICDE’97) Birmingham, England. (Apr. 1997), pp. 232–243.

[Aha92] Aha, D., Tolerating noisy, irrelevant, and novel attributes in instance-based learning algorithms, Int. J. Man-Machine Studies 36 (1992) 267–287.

[AHS96] Arabie, P.; Hubert, L.J.; De Soete, G., Clustering and Classification. (1996) World Scientific .

[AHWY03] Aggarwal, C.C.; Han, J.; Wang, J.; Yu, P.S., A framework for clustering evolving data streams, In: Proc. 2003 Int. Conf. Very Large Data Bases (VLDB’03) Berlin, Germany. (Sept. 2003), pp. 81–92.

[AHWY04a] Aggarwal, C.C.; Han, J.; Wang, J.; Yu, P.S., A framework for projected clustering of high dimensional data streams, In: Proc. 2004 Int. Conf. Very Large Data Bases (VLDB’04) Toronto, Ontario, Canada. (Aug. 2004), pp. 852–863.

[AHWY04b] Aggarwal, C.C.; Han, J.; Wang, J.; Yu, P.S., On demand classification of data streams, In: Proc. 2004 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’04) Seattle, WA. (Aug. 2004), pp. 503–508.

[AIS93] Agrawal, R.; Imielinski, T.; Swami, A., Mining association rules between sets of items in large databases, In: Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’93) Washington, DC. (May 1993), pp. 207–216.

[AK93] Anand, T.; Kahn, G., Opportunity explorer: Navigating large databases using knowledge discovery templates, In: Proc. AAAI-93 Workshop Knowledge Discovery in Databases Washington, DC. (July 1993), pp. 45–51.

[AL99] Aumann, Y.; Lindell, Y., A statistical theory for quantitative association rules, In: Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD’99) San Diego, CA. (Aug. 1999), pp. 261–270.

[All94] Allen, B.P., Case-based reasoning: Business applications, Communications of the ACM 37 (1994) 40–42.

[Alp11] Alpaydin, E., Introduction to Machine Learning. 2nd ed. (2011) MIT Press, Cambridge, MA .

[ALSS95] Agrawal, R.; Lin, K.-I.; Sawhney, H.S.; Shim, K., Fast similarity search in the presence of noise, scaling, and translation in time-series databases, In: Proc. 1995 Int. Conf. Very Large Data Bases (VLDB’95) Zurich, Switzerland. (Sept. 1995), pp. 490–501.

[AMS+96] Agrawal, R.; Mehta, M.; Shafer, J.; Srikant, R.; Arning, A.; Bollinger, T., The Quest data mining system, In: Proc. 1996 Int. Conf. Data Mining and Knowledge Discovery (KDD’96) Portland, OR. (Aug. 1996), pp. 244–249.

[Aok98] Aoki, P.M., Generalizing “search” in generalized search trees, In: Proc. 1998 Int. Conf. Data Engineering (ICDE’98) Orlando, FL. (Feb. 1998), pp. 380–389.

[AP94] Aamodt, A.; Plazas, E., Case-based reasoning: Foundational issues, methodological variations, and system approaches, AI Communications 7 (1994) 39–52.

[AP05] Angiulli, F.; Pizzuti, C., Outlier mining in large high-dimensional data sets, IEEE Trans. on Knowl. and Data Eng 17 (2005) 203–215.

[APW+99] Aggarwal, C.C.; Procopiuc, C.; Wolf, J.; Yu, P.S.; Park, J.-S., Fast algorithms for projected clustering, In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99) Philadelphia, PA. (June 1999), pp. 61–72.

[ARV09] Arora, S.; Rao, S.; Vazirani, U., Expander flows, geometric embeddings and graph partitioning, J. ACM 56 (2) (2009) 1–37.

[AS94a] Agrawal, R.; Srikant, R., Fast algorithm for mining association rules in large databases, In: Research Report RJ 9839 (June 1994) IBM Almaden Research Center, San Jose, CA.

[AS94b] Agrawal, R.; Srikant, R., Fast algorithms for mining association rules, In: Proc. 1994 Int. Conf. Very Large Data Bases (VLDB’94) Santiago, Chile. (Sept. 1994), pp. 487–499.

[AS95] Agrawal, R.; Srikant, R., Mining sequential patterns, In: Proc. 1995 Int. Conf. Data Engineering (ICDE’95)

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