• Choose a category
• All
• Classic-Fiction

[Zak00] Zaki, M.J., Scalable algorithms for association mining, IEEE Trans. Knowledge and Data Engineering 12 (2000) 372–390.

[Zak01] Zaki, M., SPADE: An efficient algorithm for mining frequent sequences, Machine Learning 40 (2001) 31–60.

[ZDN97] Zhao, Y.; Deshpande, P.M.; Naughton, J.F., An array-based algorithm for simultaneous multidimensional aggregates, In: Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’97) Tucson, AZ. (May 1997), pp. 159–170.

[ZH02] Zaki, M.J.; Hsiao, C.J., CHARM: An efficient algorithm for closed itemset mining, In: Proc. 2002 SIAM Int. Conf. Data Mining (SDM’02) Arlington, VA. (Apr. 2002), pp. 457–473.

[Zha08] Zhai, C., Statistical Language Models for Information Retrieval. (2008) Morgan and Claypool .

[ZHL+98] Zaïane, O.R.; Han, J.; Li, Z.N.; Chiang, J.Y.; Chee, S., MultiMedia-Miner: A system prototype for multimedia data mining, In: Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98) Seattle, WA. (June 1998), pp. 581–583.

[Zhu05] Zhu, X., Semi-supervised learning literature survey, In: Computer Sciences Technical Report 1530 (2005) University of Wisconsin–Madison.

[ZHZ00] Zaïane, O.R.; Han, J.; Zhu, H., Mining recurrent items in multimedia with progressive resolution refinement, In: Proc. 2000 Int. Conf. Data Engineering (ICDE’00) San Diego, CA. (Feb. 2000), pp. 461–470.

[Zia91] Ziarko, W., The discovery, analysis, and representation of data dependencies in databases, In: (Editors: Piatetsky-Shapiro, G.; Frawley, W.J.) Knowledge Discovery in Databases (1991) AAAI Press, pp. 195–209.

[ZL06] Zhou, Z.-H.; Liu, X.-Y., Training cost-sensitive neural networks with methods addressing the class imbalance problem, IEEE Trans. Knowledge and Data Engineering 18 (2006) 63–77.

[ZPOL97] Zaki, M.J.; Parthasarathy, S.; Ogihara, M.; Li, W., Parallel algorithm for discovery of association rules, Data Mining and Knowledge Discovery 1 (1997) 343–374.

[ZRL96] Zhang, T.; Ramakrishnan, R.; Livny, M., BIRCH: An efficient data clustering method for very large databases, In: Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’96) Montreal, Quebec, Canada. (June 1996), pp. 103–114.

[ZS02] Zapkowicz, N.; Stephen, S., The class imbalance program: A systematic study, Intelligence Data Analysis 6 (2002) 429–450.

[ZYH+07] Zhu, F.; Yan, X.; Han, J.; Yu, P.S.; Cheng, H., Mining colossal frequent patterns by core pattern fusion, In: Proc. 2007 Int. Conf. Data Engineering (ICDE’07) Istanbul, Turkey. (Apr. 2007), pp. 706–715.

[ZYHY07] Zhu, F.; Yan, X.; Han, J.; Yu, P.S., gPrune: A constraint pushing framework for graph pattern mining, In: Proc. 2007 Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD’07) Nanjing, China. (May 2007), pp. 388–400.

[ZZ09] Zhang, Z.; Zhang, R., Multimedia Data Mining: A Systematic Introduction to Concepts and Theory. (2009) Chapman & Hall .

[ZZH09] Zhang, D.; Zhai, C.; Han, J., Topic cube: Topic modeling for OLAP on multidimensional text databases, In: Proc. 2009 SIAM Int. Conf. Data Mining (SDM’09) Sparks, NV. (Apr. 2009), pp. 1123–1134.

Index

Numbers and Symbols

.632 bootstrap371

δ-bicluster algorithm517–518

δ-pCluster518–519

A

absolute-error criterion455

absolute support246

abstraction levels281

accuracy

attribute construction and105

boosting382

with bootstrap371

classification377–385

classifier330, 366

with cross-validation370–371

data84

with holdout method370

measures369

random forests383

with random subsampling370

rule selection based on361

activation function402

active learning25, 430, 437

ad hoc data mining31

AdaBoost380–382

algorithm illustration382

TrAdaBoost436

adaptive probabilistic networks397

advanced data analysis3, 4

advanced database systems4

affinity matrix520, 521

agglomerative hierarchical method459

AGNES459, 460

divisive hierarchical clustering versus459–460

Agglomerative Nesting (AGNES)459, 460

aggregate cells189

aggregation112

bootstrap379

complex data types