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[IGG03] Imhoff, C.; Galemmo, N.; Geiger, J.G., Mastering Data Warehouse Design: Relational and Dimensional Techniques. (2003) John Wiley & Sons .

[IKA02] Imielinski, T.; Khachiyan, L.; Abdulghani, A., Cubegrades: Generalizing association rules, Data Mining and Knowledge Discovery 6 (2002) 219–258.

[IM96] Imielinski, T.; Mannila, H., A database perspective on knowledge discovery, Communications of the ACM 39 (1996) 58–64.

[Inm96] Inmon, W.H., Building the Data Warehouse. (1996) John Wiley & Sons .

[IWM98] Inokuchi, A.; Washio, T.; Motoda, H., An apriori-based algorithm for mining frequent substructures from graph data, In: Proc. 2000 European Symp. Principles of Data Mining and Knowledge Discovery (PKDD’00) Lyon, France. (Sept. 1998), pp. 13–23.

[Jac88] Jacobs, R., Increased rates of convergence through learning rate adaptation, Neural Networks 1 (1988) 295–307.

[Jai10] Jain, A.K., Data clustering: 50 years beyond k-means, Pattern Recognition Lett. 31 (8) (2010) 651–666.

[Jam85] James, M., Classification Algorithms. (1985) John Wiley & Sons .

[JBD05] Ji, X.; Bailey, J.; Dong, G., Mining minimal distinguishing subsequence patterns with gap constraints, In: Proc. 2005 Int. Conf. Data Mining (ICDM’05) Houston, TX. (Nov. 2005), pp. 194–201.

[JD88] Jain, A.K.; Dubes, R.C., Algorithms for Clustering Data. (1988) Prentice-Hall .

[Jen96] Jensen, F.V., An Introduction to Bayesian Networks. (1996) Springer Verlag .

[JL96] John, G.H.; Langley, P., Static versus dynamic sampling for data mining, In: Proc. 1996 Int. Conf. Knowledge Discovery and Data Mining (KDD’96) Portland, OR. (Aug. 1996), pp. 367–370.

[JMF99] Jain, A.K.; Murty, M.N.; Flynn, P.J., Data clustering: A survey, ACM Computing Surveys 31 (1999) 264–323.

[Joh97]

[Joh99] John, G.H., Behind-the-scenes data mining: A report on the KDD-98 panel, SIGKDD Explorations 1 (1999) 6–8.

[JP04] Jones, N.C.; Pevzner, P.A., An Introduction to Bioinformatics Algorithms. (2004) MIT Press, Cambridge, MA .

[JSD+10] Ji, M.; Sun, Y.; Danilevsky, M.; Han, J.; Gao, J., Graph regularized transductive classification on heterogeneous information networks, In: Proc. 2010 European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD’10) Barcelona, Spain. (Sept. 2010), pp. 570–586.

[JTH01] Jin, W.; Tung, K.H.; Han, J., Mining top-n local outliers in large databases, In: Proc. 2001 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’01) San Fransisco, CA. (Aug. 2001), pp. 293–298.

[JTHW06] Jin, W.; Tung, A.K.H.; Han, J.; Wang, W., Ranking outliers using symmetric neighborhood relationship, In: Proc. 2006 Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD’06) Singapore. (Apr. 2006).

[JW92] Johnson, R.A.; Wichern, D.A., Applied Multivariate Statistical Analysis. 3rd ed. (1992) Prentice-Hall .

[JW02a] Jeh, G.; Widom, J., SimRank: A measure of structural-context similarity, In: Proc. 2002 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’02) Edmonton, Alberta, Canada. (July 2002), pp. 538–543.

[JW02b] Johnson, R.A.; Wichern, D.A., Applied Multivariate Statistical Analysis. 5th ed. (2002) Prentice Hall .

[Kam09] Kamath, C., Scientific Data Mining: A Practical Perspective. (2009) Society for Industrial and Applied Mathematic (SIAM) .

[Kas80] Kass, G.V., An exploratory technique for investigating large quantities of categorical data, Applied Statistics 29 (1980) 119–127.

[KBDM09] Kulis, B.; Basu, S.; Dhillon, I.; Mooney, R., Semi-supervised graph clustering: A kernel approach, Machine Learning 74 (2009) 1–22.

[Kec01] Kecman, V., Learning and Soft Computing. (2001) MIT Press, Cambridge, MA .

[Kei97] Keim, D.A., Visual techniques for exploring databases, In: Tutorial Notes, 3rd Int. Conf. Knowledge Discovery and Data Mining (KDD’97) Newport Beach, CA. (Aug. 1997).

[Ker92] Kerber, R., ChiMerge: Discretization of numeric attributes, In: Proc. 1992 Nat. Conf. Artificial Intelligence (AAAI’92) San Jose, CA. (1992), pp. 123–128.

[KF09] Koller, D.; Friedman, N., Probabilistic Graphical Models: Principles and