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R.; Shim, K., Walrus: A similarity retrieval algorithm for image databases, In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99) Philadelphia, PA. (June 1999), pp. 395–406.

[NW99] Nocedal, J.; Wright, S.J., Numerical Optimization. (1999) Springer Verlag .

[OFG97] Osuna, E.; Freund, R.; Girosi, F., An improved training algorithm for support vector machines, In: Proc. 1997 IEEE Workshop Neural Networks for Signal Processing (NNSP’97) Amelia Island, FL. (Sept. 1997), pp. 276–285.

[OG95] O’Neil, P.; Graefe, G., Multi-table joins through bitmapped join indices, SIGMOD Record 24 (Sept. 1995) 8–11.

[Ols03] Olson, J.E., Data Quality: The Accuracy Dimension. (2003) Morgan Kaufmann .

[Omi03] Omiecinski, E., Alternative interest measures for mining associations, IEEE Trans. Knowledge and Data Engineering 15 (2003) 57–69.

[OMM+02] O’Callaghan, L.; Meyerson, A.; Motwani, R.; Mishra, N.; Guha, S., Streaming-data algorithms for high-quality clustering, In: Proc. 2002 Int. Conf. Data Engineering (ICDE’02) San Fransisco, CA. (Apr. 2002), pp. 685–696.

[OQ97] O’Neil, P.; Quass, D., Improved query performance with variant indexes, In: Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’97) Tucson, AZ. (May 1997), pp. 38–49.

[ORS98] Özden, B.; Ramaswamy, S.; Silberschatz, A., Cyclic association rules, In: Proc. 1998 Int. Conf. Data Engineering (ICDE’98) Orlando, FL. (Feb. 1998), pp. 412–421.

[Pag89] Pagallo, G., Learning DNF by decision trees, In: Proc. 1989 Int. Joint Conf. Artificial Intelligence (IJCAI’89) San Francisco, CA. (1989), pp. 639–644.

[Paw91] Pawlak, Z., Rough Sets, Theoretical Aspects of Reasoning about Data. (1991) Kluwer Academic .

[PB00] Pinheiro, J.C.; Bates, D.M., Mixed Effects Models in S and S-PLUS. (2000) Springer Verlag .

[PBTL99] Pasquier, N.; Bastide, Y.; Taouil, R.; Lakhal, L., Discovering frequent closed itemsets for association rules, In: Proc. 7th Int. Conf. Database Theory (ICDT’99) Jerusalem, Israel. (Jan. 1999), pp. 398–416.

[PCT+03] Pan, F.; Cong, G.; Tung, A.K.H.; Yang, J.; Zaki, M., CARPENTER: Finding closed patterns in long biological datasets, In: Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’03) Washington, DC. (Aug. 2003), pp. 637–642.

[PCY95a] Park, J.S.; Chen, M.S.; Yu, P.S., An effective hash-based algorithm for mining association rules, In: Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’95) San Jose, CA. (May 1995), pp. 175–186.

[PCY95b] Park, J.S.; Chen, M.S.; Yu, P.S., Efficient parallel mining for association rules, In: Proc. 4th Int. Conf. Information and Knowledge Management Baltimore, MD. (Nov. 1995), pp. 31–36.

[Pea88] Pearl, J., Probabilistic Reasoning in Intelligent Systems. (1988) Morgan Kaufmann .

[PHL01] Pei, J.; Han, J.; Lakshmanan, L.V.S., Mining frequent itemsets with convertible constraints, In: Proc. 2001 Int. Conf. Data Engineering (ICDE’01) Heidelberg, Germany. (Apr. 2001), pp. 433–442.

[PHL+01] Pei, J.; Han, J.; Lu, H.; Nishio, S.; Tang, S.; Yang, D., H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases, In: Proc. 2001 Int. Conf. Data Mining (ICDM’01) San Jose, CA. (Nov. 2001), pp. 441–448.

[PHL04] Parsons, L.; Haque, E.; Liu, H., Subspace clustering for high dimensional data: A review, SIGKDD Explorations 6 (2004) 90–105.

[PHM00] Pei, J.; Han, J.; Mao, R., CLOSET: An efficient algorithm for mining frequent closed itemsets, In: Proc. 2000 ACM-SIGMOD Int. Workshop Data Mining and Knowledge Discovery (DMKD’00) Dallas, TX. (May 2000), pp. 11–20.

[PHM-A+01] Pei, J.; Han, J.; Mortazavi-Asl, B.; Pinto, H.; Chen, Q.; Dayal, U.; Hsu, M.-C., PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, In: Proc. 2001 Int. Conf. Data Engineering (ICDE’01) Heidelberg, Germany. (Apr. 2001), pp. 215–224.

[PHM-A+04] Pei, J.; Han, J.; Mortazavi-Asl, B.; Wang, J.; Pinto, H.; Chen, Q.; Dayal, U.; Hsu, M.-C., Mining sequential patterns by pattern-growth: The prefixSpan approach, IEEE Trans. Knowledge and Data Engineering 16 (2004) 1424–1440.

[PI97] Poosala, V.; Ioannidis,

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