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ID3, In: Proc. Fifth Int. Conf. Machine Learning (ICML’88) San Mateo, CA. (1988), pp. 107–120.

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[VC03] Vaidya, J.; Clifton, C., Privacy-preserving k-means clustering over vertically partitioned data, In: Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’03) Washington, DC. (Aug 2003).

[VC06] Vuk, M.; Curk, T., ROC curve, lift chart and calibration plot, Metodološki zvezki 3 (2006) 89–108.

[VCZ10] Vaidya, J.; Clifton, C.W.; Zhu, Y.M., Privacy Preserving Data Mining. (2010) Springer, New York .

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[VMZ06] Veloso, A.; Meira, W.; Zaki, M., Lazy associative classificaiton, In: Proc. 2006 Int. Conf. Data Mining (ICDM’06) Hong Kong, China. (2006), pp. 645–654.

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[VWI98] Vitter, J.S.; Wang, M.; Iyer, B.R., Data cube approximation and histograms via wavelets, In: Proc. 1998 Int. Conf. Information and Knowledge Management (CIKM’98) Washington, DC. (Nov. 1998), pp. 96–104.

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[Wat03] Watts, D.J., Six Degrees: The Science of a Connected Age. (2003) W. W. Norton & Company .

[WB98] Westphal, C.; Blaxton, T., Data Mining Solutions: Methods and Tools for Solving Real-World Problems. (1998) John Wiley & Sons .

[WCH10] Wu, T.; Chen, Y.; Han, J., Re-examination of interestingness measures in pattern mining: A unified framework, Data Mining and Knowledge Discovery 21 (3) (2010) 371–397.

[WCRS01] Wagstaff, K.; Cardie, C.; Rogers, S.; Schrödl, S., Constrained k-means clustering with background knowledge, In: Proc. 2001 Int. Conf. Machine Learning (ICML’01) Williamstown, MA. (June 2001), pp. 577–584.

[Wei04] Weiss, G.M., Mining with rarity: A unifying framework, SIGKDD Explorations 6 (2004) 7–19.

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[WF05] Witten, I.H.; Frank, E., Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. (2005) Morgan Kaufmann .

[WFH11] Witten, I.H.; Frank, E.; Hall, M.A., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. 3rd ed. (2011) Morgan Kaufmann, Boston .

[WFYH03] Wang, H.; Fan, W.; Yu, P.S.; Han, J., Mining concept-drifting data streams using ensemble classifiers, In: Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’03) Washington, DC. (Aug. 2003), pp. 226–235.

[WHH00] Wang, K.; He, Y.; Han, J., Mining frequent itemsets using support constraints, In: Proc. 2000 Int. Conf. Very Large Data Bases (VLDB’00) Cairo, Egypt. (Sept. 2000), pp. 43–52.

[WHJ+10] Wang, C.; Han, J.; Jia, Y.; Tang, J.; Zhang, D.; Yu, Y.; Guo, J., Mining advisor-advisee relationships from research publication networks, In: Proc. 2010 ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD’10) Washington, DC. (July 2010).

[WHLT05] Wang, J.; Han, J.; Lu, Y.; Tzvetkov, P., TFP: An efficient algorithm for mining top-k frequent closed itemsets, IEEE Trans. Knowledge and Data Engineering 17 (2005) 652–664.

[WHP03] Wang, J.; Han, J.; Pei, J., CLOSET+: Searching for the best strategies for mining frequent closed itemsets, In: Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’03) Washington, DC. (Aug. 2003), pp. 236–245.

[WI98] Weiss, S.M.; Indurkhya, N., Predictive Data Mining. (1998) Morgan Kaufmann

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