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[Wid95] Widom, J., Research problems in data warehousing, In: Proc. 4th Int. Conf. Information and Knowledge Management Baltimore, MD. (Nov. 1995), pp. 25–30.

[WIZD04] Weiss, S.; Indurkhya, N.; Zhang, T.; Damerau, F., Text Mining: Predictive Methods for Analyzing Unstructured Information. (2004) Springer, New York .

[WK91] Weiss, S.M.; Kulikowski, C.A., Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. (1991) Morgan Kaufmann .

[WK05] Wang, J.; Karypis, G., HARMONY: Efficiently mining the best rules for classification, In: Proc. 2005 SIAM Conf. Data Mining (SDM’05) Newport Beach, CA. (Apr. 2005), pp. 205–216.

[WLFY02] Wang, W.; Lu, H.; Feng, J.; Yu, J.X., Condensed cube: An effective approach to reducing data cube size, In: Proc. 2002 Int. Conf. Data Engineering (ICDE’02) San Fransisco, CA. (Apr. 2002), pp. 155–165.

[WRL94] Widrow, B.; Rumelhart, D.E.; Lehr, M.A., Neural networks: Applications in industry, business and science, Communications of the ACM 37 (1994) 93–105.

[WSF95] Wang, R.; Storey, V.; Firth, C., A framework for analysis of data quality research, IEEE Trans. Knowledge and Data Engineering 7 (1995) 623–640.

[Wu83] Wu, C.F.J., On the convergence properties of the EM algorithm, Ann. Statistics 11 (1983) 95–103.

[WW96] Wand, Y.; Wang, R., Anchoring data quality dimensions in ontological foundations, Communications of the ACM 39 (1996) 86–95.

[WWYY02] Wang, H.; Wang, W.; Yang, J.; Yu, P.S., Clustering by pattern similarity in large data sets, In: Proc. 2002 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’02) Madison, WI. (June 2002), pp. 418–427.

[WXH08] Wu, T.; Xin, D.; Han, J., ARCube: Supporting ranking aggregate queries in partially materialized data cubes, In: Proc. 2008 ACM SIGMOD Int. Conf. Management of Data (SIGMOD’08) Vancouver, British Columbia, Canada. (June 2008), pp. 79–92.

[WXMH09] Wu, T.; Xin, D.; Mei, Q.; Han, J., Promotion analysis in multi-dimensional space, Lyon, France. Proc. 2009 Int. Conf. Very Large Data Bases (VLDB’09) 2 (1) (Aug. 2009) 109–120.

[WYM97] Wang, W.; Yang, J.; Muntz, R., STING: A statistical information grid approach to spatial data mining, In: Proc. 1997 Int. Conf. Very Large Data Bases (VLDB’97) Athens, Greece. (Aug. 1997), pp. 186–195.

[XCYH06] Xin, D.; Cheng, H.; Yan, X.; Han, J., Extracting redundancy-aware top-k patterns, In: Proc. 2006 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’06) Philadelphia, PA. (Aug. 2006), pp. 444–453.

[XHCL06] Xin, D.; Han, J.; Cheng, H.; Li, X., Answering top-k queries with multi-dimensional selections: The ranking cube approach, In: Proc. 2006 Int. Conf. Very Large Data Bases (VLDB’06) Seoul, Korea. (Sept. 2006), pp. 463–475.

[XHLW03] Xin, D.; Han, J.; Li, X.; Wah, B.W., Star-cubing: Computing iceberg cubes by top-down and bottom-up integration, In: Proc. 2003 Int. Conf. Very Large Data Bases (VLDB’03) Berlin, Germany. (Sept. 2003), pp. 476–487.

[XHSL06] Xin, D.; Han, J.; Shao, Z.; Liu, H., C-cubing: Efficient computation of closed cubes by aggregation-based checking, In: Proc. 2006 Int. Conf. Data Engineering (ICDE’06) Atlanta, GA. (Apr. 2006), p. 4.

[XHYC05] Xin, D.; Han, J.; Yan, X.; Cheng, H., Mining compressed frequent-pattern sets, In: Proc. 2005 Int. Conf. Very Large Data Bases (VLDB’05) Trondheim, Norway. (Aug. 2005), pp. 709–720.

[XOJ00] Xiang, Y.; Olesen, K.G.; Jensen, F.V., Practical issues in modeling large diagnostic systems with multiply sectioned Bayesian networks, Intl. J. Pattern Recognition and Artificial Intelligence (IJPRAI) 14 (2000) 59–71.

[XPK10] Xing, Z.; Pei, J.; Keogh, E., A brief survey on sequence classification, SIGKDD Explorations 12 (2010) 40–48.

[XSH+04] Xiong, H.; Shekhar, S.; Huang, Y.; Kumar, V.; Ma, X.; Yoo, J.S., A framework for discovering co-location patterns in data sets with extended spatial objects, In: Proc. 2004 SIAM Int. Conf. Data Mining (SDM’04) Lake Buena Vista, FL. (Apr. 2004).

[XYFS07] Xu, X.; Yuruk, N.; Feng, Z.; Schweiger, T.A.J., SCAN: A structural clustering algorithm