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[MB88] McLachlan, G.J.; Basford, K.E., Mixture Models: Inference and Applications to Clustering. (1988) John Wiley & Sons .

[MC03] Mahoney, M.V.; Chan, P.K., Learning rules for anomaly detection of hostile network traffic, In: Proc. 2003 Int. Conf. Data Mining (ICDM’03) Melbourne, FL. (Nov. 2003).

[MCK+04] Mamoulis, N.; Cao, H.; Kollios, G.; Hadjieleftheriou, M.; Tao, Y.; Cheung, D., Mining, indexing, and querying historical spatiotemporal data, In: Proc. 2004 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’04) Seattle, WA. (Aug. 2004), pp. 236–245.

[MCM83] Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M., Machine Learning, An Artificial Intelligence Approach. (1983) Morgan Kaufmann ; Vol. 1.

[MCM86] Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M., Machine Learning, An Artificial Intelligence Approach. (1986) Morgan Kaufmann ; Vol. 2.

[MD88] Muralikrishna, M.; DeWitt, D.J., Equi-depth histograms for extimating selectivity factors for multi-dimensional queries, In: Proc. 1988 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’88) Chicago, IL. (June 1988), pp. 28–36.

[Mei03] Meilǎ, M., Comparing clusterings by the variation of information, In: Proc. 16th Annual Conf. Computational Learning Theory (COLT’03) Washington, DC. (Aug. 2003), pp. 173–187.

[Mei05] Meilǎ, M., Comparing clusterings: An axiomatic view, In: Proc. 22nd Int. Conf. Machine Learning (ICML’05) Bonn, Germany. (2005), pp. 577–584.

[Men03] Mena, J., Investigative Data Mining with Security and Criminal Detection. (2003) Butterworth-Heinemann .

[MFS95] Malerba, D.; Floriana, E.; Semeraro, G., A further comparison of simplification methods for decision tree induction, In: (Editors: Fisher, D.; Lenz, H.) Learning from Data: AI and Statistics (1995) Springer Verlag.

[MH95] Martin, J.K.; Hirschberg, D.S., The time complexity of decision tree induction, In: Technical Report ICS-TR 95-27 Department of Information and Computer Science, University of California, Irvine, CA. (Aug. 1995), pp. 1–27.

[MH09] Miller, H.; Han, J., Geographic Data Mining and Knowledge Discovery. 2nd ed. (2009) Chapman & Hall/CRC .

[Mic83] Michalski, R.S., A theory and methodology of inductive learning, In: (Editors: Michalski, R.S.; Carbonell, J.G.; Mitchell, T.M.) Machine Learning: An Artificial Intelligence Approach, Vol. 1 (1983) Morgan Kaufmann, pp. 83–134.

[Mic92] Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs. (1992) Springer Verlag .

[Mil98] Miller, R.G., Survival Analysis. (1998) Wiley-Interscience .

[Min89] Mingers, J., An empirical comparison of pruning methods for decision-tree induction, Machine Learning 4 (1989) 227–243.

[Mir98] Mirkin, B., Mathematical classification and clustering, J. Global Optimization 12 (1998) 105–108.

[Mit96] Mitchell, M., An Introduction to Genetic Algorithms. (1996) MIT Press, Cambridge, MA .

[Mit97] Mitchell, T.M., Machine Learning. (1997) McGraw-Hill .

[MK91] Manago, M.; Kodratoff, Y., Induction of decision trees from complex structured data, In: (Editors: Piatetsky-Shapiro, G.; Frawley, W.J.) Knowledge Discovery in Databases (1991) AAAI/MIT Press, pp. 289–306.

[MLSZ06] Mei, Q.; Liu, C.; Su, H.; Zhai, C., A probabilistic approach to spatiotemporal theme pattern mining on weblogs, In: Proc. 15th Int. Conf. World Wide Web (WWW’06) Edinburgh, Scotland. (May 2006), pp. 533–542.

[MM95] Major, J.; Mangano, J., Selecting among rules induced from a hurricane database, J. Intelligent Information Systems 4 (1995) 39–52.

[MM02] Manku, G.; Motwani, R., Approximate frequency counts over data streams, In: Proc. 2002 Int. Conf. Very Large Data Bases (VLDB’02) Hong Kong, China. (Aug. 2002), pp. 346–357.

[MN89] Mézard, M.; Nadal, J.-P., Learning in feedforward layered networks: The tiling algorithm, J. Physics 22 (1989) 2191–2204.

[MO04] Madeira, S.C.; Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey, IEEE/ACM Trans. Computational Biology and Bioinformatics 1 (1) (2004) 24–25.

[MP69] Minsky, M.L.; Papert,