Databases Reference
In-Depth Information
[AGS97] R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In
Proc. 1997
Int. Conf. Data Engineering (ICDE'97)
, pp. 232-243, Birmingham, England, Apr. 1997.
[Aha92] D. Aha. Tolerating noisy, irrelevant, and novel attributes in instance-based learning
algorithms.
Int. J. Man-Machine Studies
, 36:267-287, 1992.
[AHS96] P. Arabie, L. J. Hubert, and G. De Soete.
Clustering and Classification
. World Scientific,
1996.
[AHWY03] C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data
streams. In
Proc. 2003 Int. Conf. Very Large Data Bases (VLDB'03)
, pp. 81-92, Berlin,
Germany, Sept. 2003.
[AHWY04a] C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for projected cluster-
ing of high dimensional data streams. In
Proc. 2004 Int. Conf. Very Large Data Bases
(VLDB'04)
, pp. 852-863, Toronto, Ontario, Canada, Aug. 2004.
[AHWY04b] C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. On demand classification of data streams.
In
Proc. 2004 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD'04)
,
pp. 503-508, Seattle, WA, Aug. 2004.
[AIS93] R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of
items in large databases. In
Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data
(SIGMOD'93)
, pp. 207-216, Washington, DC, May 1993.
[AK93] T. Anand and G. Kahn. Opportunity explorer: Navigating large databases using knowl-
edge discovery templates. In
Proc. AAAI-93 Workshop Knowledge Discovery in Databases
,
pp. 45-51, Washington, DC, July 1993.
[AL99] Y. Aumann and Y. Lindell. A statistical theory for quantitative association rules. In
Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99)
, pp. 261-270,
San Diego, CA, Aug. 1999.
[All94] B. P. Allen. Case-based reasoning: Business applications.
Communications of the ACM
,
37:40-42, 1994.
[Alp11] E. Alpaydin.
Introduction to Machine Learning
(2nd ed.). Cambridge, MA: MIT Press,
2011.
[ALSS95] R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence
of noise, scaling, and translation in time-series databases. In
Proc. 1995 Int. Conf. Very
Large Data Bases (VLDB'95)
, pp. 490-501, Zurich, Switzerland, Sept. 1995.
[AMS
C
96] R. Agrawal, M. Mehta, J. Shafer, R. Srikant, A. Arning, and T. Bollinger. The Quest data
mining system. In
Proc. 1996 Int. Conf. Data Mining and Knowledge Discovery (KDD'96)
,
pp. 244-249, Portland, OR, Aug. 1996.
[Aok98]
P. M. Aoki. Generalizing “search” in generalized search trees. In
Proc. 1998 Int. Conf.
Data Engineering (ICDE'98)
, pp. 380-389, Orlando, FL, Feb. 1998.
[AP94]
A. Aamodt and E. Plazas. Case-based reasoning: Foundational issues, methodological
variations, and system approaches.
AI Communications
, 7:39-52, 1994.
[AP05]
F. Angiulli, and C. Pizzuti. Outlier mining in large high-dimensional data sets.
IEEE
Trans. on Knowl. and Data Eng
., 17:203-215, 2005.
[APW
C
99]
C. C. Aggarwal, C. Procopiuc, J. Wolf, P. S. Yu, and J.-S. Park. Fast algorithms for
projected clustering. In
Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data
(SIGMOD'99)
, pp. 61-72, Philadelphia, PA, June 1999.
[ARV09]
S. Arora, S. Rao, and U. Vazirani. Expander flows, geometric embeddings and graph
partitioning.
J. ACM
, 56(2):1-37, 2009.