HAN
21-bib-633-672-9780123814791
2011/6/1
3:27
Page 667
#35
Bibliography
667
[VC06]
M. Vuk and T. Curk. ROC curve, lift chart and calibration plot.
Metodoloˇski zvezki,
3:89–108, 2006.
[VCZ10]
J. Vaidya, C. W. Clifton, and Y. M. Zhu. Privacy Preserving Data Mining. New York:
Springer, 2010.
[VGK02]
M. Vlachos, D. Gunopulos, and G. Kollios. Discovering similar multidimensional trajec-
tories. In Proc. 2002 Int. Conf. Data Engineering (ICDE’02), pp. 673–684, San Fransisco,
CA, Apr. 2002.
[VMZ06]
A. Veloso, W. Meira, and M. Zaki. Lazy associative classificaiton. In
Proc. 2006 Int. Conf.
Data Mining (ICDM’06), pp. 645–654, Hong Kong, China, 2006.
[vR90]
C. J. van Rijsbergen.
Information Retrieval. Butterworth, 1990.
[VWI98]
J. S. Vitter, M. Wang, and B. R. Iyer. Data cube approximation and histograms via
wavelets. In Proc. 1998 Int. Conf. Information and Knowledge Management (CIKM’98),
pp. 96–104, Washington, DC, Nov. 1998.
[Wat95]
M. S. Waterman.
Introduction to Computational Biology: Maps, Sequences, and Genomes
(Interdisciplinary Statistics). CRC Press, 1995.
[Wat03]
D. J. Watts.
Six Degrees: The Science of a Connected Age. W. W. Norton & Company, 2003.
[WB98]
C. Westphal and T. Blaxton.
Data Mining Solutions: Methods and Tools for Solving Real-
World Problems. John Wiley & Sons, 1998.
[WCH10]
T. Wu, Y. Chen, and J. Han. Re-examination of interestingness measures in pattern
mining: A unified framework. Data Mining and Knowledge Discovery, 21(3):371–397,
2010.
[WCRS01]
K. Wagstaff, C. Cardie, S. Rogers, and S. Schr¨odl. Constrained
k-means clustering with
background knowledge. In Proc. 2001 Int. Conf. Machine Learning (ICML’01), pp. 577–
584, Williamstown, MA, June 2001.
[Wei04]
G. M. Weiss. Mining with rarity: A unifying framework.
SIGKDD Explorations, 6:7–19,
2004.
[WF94]
S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cam-
bridge University Press, 1994.
[WF05]
I. H. Witten and E. Frank.
Data Mining: Practical Machine Learning Tools and Techniques
(2nd ed.). Morgan Kaufmann, 2005.
[WFH11]
I. H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Machine Learning Tools
and Techniques with Java Implementations (3rd ed.). Boston: Morgan Kaufmann, 2011.
[WFYH03]
H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using
ensemble classifiers. In Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and
Data Mining (KDD’03), pp. 226–235, Washington, DC, Aug. 2003.
[WHH00]
K. Wang, Y. He, and J. Han. Mining frequent itemsets using support constraints. In
Proc. 2000 Int. Conf. Very Large Data Bases (VLDB’00), pp. 43–52, Cairo, Egypt, Sept.
2000.
[WHJ
+
10]
C. Wang, J. Han, Y. Jia, J. Tang, D. Zhang, Y. Yu, and J. Guo. 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]
J. Wang, J. Han, Y. Lu, and P. Tzvetkov. TFP: An efficient algorithm for mining top-
k
frequent closed itemsets. IEEE Trans. Knowledge and Data Engineering, 17:652–664,
2005.
HAN
21-bib-633-672-9780123814791
2011/6/1
3:27
Page 668
#36
668
Bibliography
[WHP03]
J. Wang, J. Han, and J. Pei. CLOSET+: Searching for the best strategies for mining fre-
quent closed itemsets. In Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and
Data Mining (KDD’03), pp. 236–245, Washington, DC, Aug. 2003.
[WI98]
S. M. Weiss and N. Indurkhya.
Predictive Data Mining. Morgan Kaufmann, 1998.
[Wid95]
J. Widom. Research problems in data warehousing. In
Proc. 4th Int. Conf. Information
and Knowledge Management, pp. 25–30, Baltimore, MD, Nov. 1995.
[WIZD04]
S. Weiss, N. Indurkhya, T. Zhang, and F. Damerau.
Text Mining: Predictive Methods for
Analyzing Unstructured Information. New York: Springer, 2004.
[WK91]
S. M. Weiss and C. A. Kulikowski.
Computer Systems That Learn: Classification and
Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems.
Morgan Kaufmann, 1991.
[WK05]
J. Wang and G. Karypis. HARMONY: Efficiently mining the best rules for classification.
In
Proc. 2005 SIAM Conf. Data Mining (SDM’05), pp. 205–216, Newport Beach, CA,
Apr. 2005.
[WLFY02]
W. Wang, H. Lu, J. Feng, and J. X. Yu. Condensed cube: An effective approach to reduc-
ing data cube size. In Proc. 2002 Int. Conf. Data Engineering (ICDE’02), pp. 155–165,
San Fransisco, CA, Apr. 2002.
[WRL94]
B. Widrow, D. E. Rumelhart, and M. A. Lehr. Neural networks: Applications in industry,
business and science.
Communications of the ACM, 37:93–105, 1994.
[WSF95]
R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality research.
IEEE
Trans. Knowledge and Data Engineering, 7:623–640, 1995.
[Wu83]
C. F. J. Wu. On the convergence properties of the EM algorithm.
Ann. Statistics, 11:95–
103, 1983.
[WW96]
Y. Wand and R. Wang. Anchoring data quality dimensions in ontological foundations.
Communications of the ACM, 39:86–95, 1996.
[WWYY02]
H. Wang, W. Wang, J. Yang, and P. S. Yu. Clustering by pattern similarity in large
data sets. In Proc. 2002 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’02),
pp. 418–427, Madison, WI, June 2002.
[WXH08]
T. Wu, D. Xin, and J. Han. ARCube: Supporting ranking aggregate queries in partially
materialized data cubes. In Proc. 2008 ACM SIGMOD Int. Conf. Management of Data
(SIGMOD’08), pp. 79–92, Vancouver, British Columbia, Canada, June 2008.
[WXMH09]
T. Wu, D. Xin, Q. Mei, and J. Han. Promotion analysis in multi-dimensional space. In
Proc. 2009 Int. Conf. Very Large Data Bases (VLDB’09), 2(1):109–120, Lyon, France, Aug.
2009.
[WYM97]
W. Wang, J. Yang, and R. Muntz. STING: A statistical information grid approach
to spatial data mining. In Proc. 1997 Int. Conf. Very Large Data Bases (VLDB’97),
pp. 186–195, Athens, Greece, Aug. 1997.
[XCYH06]
D. Xin, H. Cheng, X. Yan, and J. Han. Extracting redundancy-aware top-k patterns.
In Proc. 2006 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’06),
pp. 444–453, Philadelphia, PA, Aug. 2006.
[XHCL06]
D. Xin, J. Han, H. Cheng, and X. Li. Answering top-k queries with multi-dimensional
selections: The ranking cube approach. In Proc. 2006 Int. Conf. Very Large Data Bases
(VLDB’06), pp. 463–475, Seoul, Korea, Sept. 2006.