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[MRA95] Metha, M.; Rissanen, J.; Agrawal, R., MDL-based decision tree pruning, In: Proc. 1995 Int. Conf. Knowledge Discovery and Data Mining (KDD’95) Montreal, Quebec, Canada. (Aug. 1995), pp. 216–221.

[MRS08] Manning, C.D.; Raghavan, P.; Schutze, H., Introduction to Information Retrieval. (2008) Cambridge University Press .

[MS03a] Markou, M.; Singh, S., Novelty detection: A review—part 1: Statistical approaches, Signal Processing 83 (2003) 2481–2497.

[MS03b] Markou, M.; Singh, S., Novelty detection: A review—part 2: Neural network based approaches, Signal Processing 83 (2003) 2499–2521.

[MST94] Michie, D.; Spiegelhalter, D.J.; Taylor, C.C., Machine Learning, Neural and Statistical Classification. (1994) Ellis Horwood, Chichester, England .

[MT94] Michalski, R.S.; Tecuci, G., Machine Learning, A Multistrategy Approach. (1994) Morgan Kaufmann ; Vol. 4.

[MTV94] Mannila, H.; Toivonen, H.; Verkamo, A.I., Efficient algorithms for discovering association rules, In: Proc. AAAI’94 Workshop Knowledge Discovery in Databases (KDD’94) Seattle, WA. (July 1994), pp. 181–192.

[MTV97] Mannila, H.; Toivonen, H.; Verkamo, A.I., Discovery of frequent episodes in event sequences, Data Mining and Knowledge Discovery 1 (1997) 259–289.

[Mur98] Murthy, S.K., Automatic construction of decision trees from data: A multi-disciplinary survey, Data Mining and Knowledge Discovery 2 (1998) 345–389.

[Mut05]

[MXC+07] Mei, Q.; Xin, D.; Cheng, H.; Han, J.; Zhai, C., Semantic annotation of frequent patterns, ACM Trans. Knowledge Discovery from Data (TKDD) 15 (2007) 321–348.

[MY97] Miller, R.J.; Yang, Y., Association rules over interval data, In: Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’97) Tucson, AZ. (May 1997), pp. 452–461.

[MZ06] Mei, Q.; Zhai, C., A mixture model for contextual text mining, In: Proc. 2006 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’06) Philadelphia, PA. (Aug. 2006), pp. 649–655.

[NB86] Niblett, T.; Bratko, I., Learning decision rules in noisy domains, In: (Editor: Brammer, M.A.) Expert Systems ’86: Research and Development in Expert Systems III (Dec. 1986) British Computer Society Specialist Group on Expert Systems, pp. 25–34.

[NBW06] Newman, M.; Barabasi, A.-L.; Watts, D.J., The Structure and Dynamics of Networks. (2006) Princeton University Press .

[NC03] Noble, C.C.; Cook, D.J., Graph-based anomaly detection, In: Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’03) Washington, DC. (Aug. 2003), pp. 631–636.

[New10] Newman, M., Networks: An Introduction. (2010) Oxford University Press .

[NG04] Newman, M.E.J.; Girvan, M., Finding and evaluating community structure in networks, Physical Rev. E 69 (2004) 113–128.

[NGE-R09] Neville, J.; Gallaher, B.; Eliassi-Rad, T., Evaluating statistical tests for within-network classifiers of relational data, In: Proc. 2009 Int. Conf. Data Mining (ICDM’09) Miami, FL. (Dec. 2009), pp. 397–406.

[NH94] Ng, R.; Han, J., Efficient and effective clustering method for spatial data mining, In: Proc. 1994 Int. Conf. Very Large Data Bases (VLDB’94) Santiago, Chile. (Sept. 1994), pp. 144–155.

[NJW01] Ng, A.Y.; Jordan, M.I.; Weiss, Y., On spectral clustering: Analysis and an algorithm, In: (Editors: Dietterich, T.G.; Becker, S.; Ghahramani, Z.) Advances in Neural Information Processing Systems 14 (2001) MIT Press, Cambridge, MA, pp. 849–856.

[NK04] Nijssen, S.; Kok, J., A quick start in frequent structure mining can make a difference, In: Proc. 2004 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’04) Seattle, WA. (Aug. 2004), pp. 647–652.

[NKNW96] Neter, J.; Kutner, M.H.; Nachtsheim, C.J.; Wasserman, L., Applied Linear Statistical Models. 4th ed. (1996) Irwin .

[NLHP98] Ng, R.; Lakshmanan, L.V.S.; Han, J.; Pang, A., Exploratory mining and pruning optimizations of constrained associations rules, In: Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98) Seattle, WA. (June 1998), pp. 13–24.

[NRS99] Natsev, A.; Rastogi,