A framework for measuring changes in data characteristics

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A framework for measuring changes in data characteristics

Full text

Pdf (1.53 MB)

Source

Symposium on Principles of Database Systems archive
Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems table of contents

Philadelphia, Pennsylvania, United States

Pages: 126 - 137

Year of Publication: 1999

ISBN:1-58113-062-7

Authors

Venkatesh Ganti
Johannes Gehrke
Raghu Ramakrishnan

Sponsors

SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 3, Downloads (12 Months): 40, Citation Count: 24

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/303976.303989 What is a DOI?

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Rakesh Agrawal , Johannes Gehrke , Dimitrios Gunopulos , Prabhakar Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.94-105, June 01-04, 1998, Seattle, Washington, United States
	2	R. Agrawal , T. Imielinski , A. Swami, Database Mining: A Performance Perspective, IEEE Transactions on Knowledge and Data Engineering, v.5 n.6, p.914-925, December 1993 [doi>10.1109/69.250074]
	3	Rakesh Agrawal , Heikki Mannila , Ramakrishnan Srikant , Hannu Toivonen , A. Inkeri Verkamo, Fast discovery of association rules, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	4	Rakesh Agrawal and Giuseppe Psaila. Acive data mining. Proceedings of the first international conference on knowledge discovery and data mining, 1995.
	5	Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
	6	Andreas Aming, Rakesh Agrawal, and Prabhakar Raghavan. A linear method for deviation detection in large databases. In Proc. of the 2nd Int'l Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, August 1996.
	7	Peter J. Bickel and Kjell A. Doksum. Mathematical Statistics: Basic Ideas and Selected Topics. Prentice Hall, 1976.
	8	L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1984.
	9	Sergey Brin , Rajeev Motwani , Jeffrey D. Ullman , Shalom Tsur, Dynamic itemset counting and implication rules for market basket data, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.255-264, May 11-15, 1997, Tucson, Arizona, United States
	10	Soumen Chakrabarti , Sunita Sarawagi , Byron Dom, Mining Surprising Patterns Using Temporal Description Length, Proceedings of the 24rd International Conference on Very Large Data Bases, p.606-617, August 24-27, 1998
	11	David Wai-Lok Cheung , Jiawei Han , Vincent Ng , C. Y. Wong, Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique, Proceedings of the Twelfth International Conference on Data Engineering, p.106-114, February 26-March 01, 1996
	12	E. F. Codd. Providing OLAP (on-line analytical processing) to useranalysts: An IT mandate. Technical report, E. F. Codd and Associates, 1993.
	13	Ralph B D'Agostino , Michael A Stephens, Goodness-of-fit techniques, Marcel Dekker, Inc., New York, NY, 1986
	14	Bradley Efron and Robert J. Tibshirani. An introduction to the bootstrap. Chapman & Hall, 1993.
	15	Martin Ester, Hans-Peter Kriegel, and Xiaowei Xu. A database interface for clustering in large spatial databases. In Proc. of the 1st lnt'l Conference on Knowledge Discovery in Databases and Data Mining, Montreal, Canada, August 1995.
	16	Usama M. Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth , Ramasamy Uthurusamy, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	17	Ronen Feldman, Yonatan Aumann, Amihood Amir, and Heikki Mannila. Efficient algorithms for discovering frequent sets in incremental databases. Workshop on Research issues on Data Mining and Knowledge Discovery, 1997.
	18	Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan, and Wei- Yin Loh. A framework for measuring changes in data characteristics. http://www.cs.wisc.edu/vganti/pods99-fuU.ps, November 1998.
	19	Venkatesh Ganti , Raghu Ramakrishnan , Johannes Gehrke , Allison Powell, Clustering Large Datasets in Arbitrary Metric Spaces, Proceedings of the 15th International Conference on Data Engineering, p.502, March 23-26, 1999
	20	Johannes Gehrke , Raghu Ramakrishnan , Venkatesh Ganti, RainForest - A Framework for Fast Decision Tree Construction of Large Datasets, Proceedings of the 24rd International Conference on Very Large Data Bases, p.416-427, August 24-27, 1998
	21	David Gibson , Jon M. Kleinberg , Prabhakar Raghavan, Clustering Categorical Data: An Approach Based on Dynamical Systems, Proceedings of the 24rd International Conference on Very Large Data Bases, p.311-322, August 24-27, 1998
	22	George Gratzer. Lattice Theory: First Concepts and Distributive Lattices. W. H. Freemand and Company, 1970.
	23	Sudipto Guha , Rajeev Rastogi , Kyuseok Shim, CURE: an efficient clustering algorithm for large databases, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.73-84, June 01-04, 1998, Seattle, Washington, United States
	24	ROCK: A Robust Clustering Algorithm for Categorical Attributes, Proceedings of the 15th International Conference on Data Engineering, p.512, March 23-26, 1999
	25	Isabelle Guyon , Nada Matic , Vladimir Vapnik, Discovering informative patterns and data cleaning, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	26	Tomasz Imielinski , Heikki Mannila, A database perspective on knowledge discovery, Communications of the ACM, v.39 n.11, p.58-64, Nov. 1996 [doi>10.1145/240455.240472]
	27	Edwin M. Knorr , Raymond T. Ng, Algorithms for Mining Distance-Based Outliers in Large Datasets, Proceedings of the 24rd International Conference on Very Large Data Bases, p.392-403, August 24-27, 1998
	28	Manish Mehta , Rakesh Agrawal , Jorma Rissanen, SLIQ: A Fast Scalable Classifier for Data Mining, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, p.18-32, March 25-29, 1996
	29	Raymond T. Ng , Jiawei Han, Efficient and Effective Clustering Methods for Spatial Data Mining, Proceedings of the 20th International Conference on Very Large Data Bases, p.144-155, September 12-15, 1994
	30	Raymond T. Ng , Laks V. S. Lakshmanan , Jiawei Han , Alex Pang, Exploratory mining and pruning optimizations of constrained associations rules, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.13-24, June 01-04, 1998, Seattle, Washington, United States
	31	Jong Soo Park , Ming-Syan Chen , Philip S. Yu, An effective hash-based algorithm for mining association rules, Proceedings of the 1995 ACM SIGMOD international conference on Management of data, p.175-186, May 22-25, 1995, San Jose, California, United States
	32	Rajeev Rastogi , Kyuseok Shim, PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning, Proceedings of the 24rd International Conference on Very Large Data Bases, p.404-415, August 24-27, 1998
	33	Ashoka Savasere , Edward Omiecinski , Shamkant B. Navathe, An Efficient Algorithm for Mining Association Rules in Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.432-444, September 11-15, 1995
	34	John C. Shafer , Rakesh Agrawal , Manish Mehta, SPRINT: A Scalable Parallel Classifier for Data Mining, Proceedings of the 22th International Conference on Very Large Data Bases, p.544-555, September 03-06, 1996
	35	Jude W. Shavlik , Thomas E. Deitterich , Thomas Dietterich, Readings in Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991
	36	Avi Silberschatz , Alexander Tuzhilin, What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, v.8 n.6, p.970-974, December 1996 [doi>10.1109/69.553165]
	37	Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, and Sanjay Ranka. An efficient algorithm for the incremental updation of association rules in large databases. In Proceedings of 3rd International Conference on Knowledge Discovery in Databases, 1997.
	38	Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: an efficient data clustering method for very large databases, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.103-114, June 04-06, 1996, Montreal, Quebec, Canada

CITED BY 24

	Venkatesh Ganti , Johannes Gehrke , Raghu Ramakrishnan, Mining data streams under block evolution, ACM SIGKDD Explorations Newsletter, v.3 n.2, January 2002
	Bing Liu , Wynne Hsu , Yiming Ma, Discovering the set of fundamental rule changes, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.335-340, August 26-29, 2001, San Francisco, California
	Venkatesh Ganti , Raghu Ramakrishnan, Mining and monitoring evolving data, Handbook of massive data sets, Kluwer Academic Publishers, Norwell, MA, 2002
	Charu C. Aggarwal, A framework for diagnosing changes in evolving data streams, Proceedings of the 2003 ACM SIGMOD international conference on Management of data, June 09-12, 2003, San Diego, California
	Elias Pampalk , Werner Goebl , Gerhard Widmer, Visualizing changes in the structure of data for exploratory feature selection, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	Tao Li , Shenghuo Zhu , Mitsunori Ogihara, A new distributed data mining model based on similarity, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida
	Zhiyuan Chen , Chen Li , Jian Pei , Yufei Tao , Haixun Wang , Wei Wang , Jiong Yang , Jun Yang , Donghui Zhang, Recent progress on selected topics in database research: a report by nine young Chinese researchers working in the United States, Journal of Computer Science and Technology, v.18 n.5, p.538-552, September 2003
	Charu C. Aggarwal, On Change Diagnosis in Evolving Data Streams, IEEE Transactions on Knowledge and Data Engineering, v.17 n.5, p.587-600, May 2005
	Venkatesh Ganti , Johannes Gehrke , Raghu Ramakrishnan, DEMON: Mining and Monitoring Evolving Data, IEEE Transactions on Knowledge and Data Engineering, v.13 n.1, p.50-63, January 2001
	Samuel Kaski , Janne Nikkila , Janne Sinkkonen , Leo Lahti , Juha E. A. Knuuttila , Christophe Roos, Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), v.2 n.3, p.203-216, July 2005
	Qiankun Zhao , Sourav S. Bhowmick , Le Gruenwald, WAM-Miner: in the search of web access motifs from historical web log data, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany
	Myra Spiliopoulou , Irene Ntoutsi , Yannis Theodoridis , Rene Schult, MONIC: modeling and monitoring cluster transitions, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
	Hua-Fu Li , Suh-Yin Lee , Man-Kwan Shan, DSM-PLW: single-pass mining of path traversal patterns over streaming web click-sequences, Computer Networks: The International Journal of Computer and Telecommunications Networking, v.50 n.10, p.1474-1487, 14 July 2006
	Tao Li , Mitsunori Ogihara , Shenghuo Zhu, Association-based similarity testing and its applications, Intelligent Data Analysis, v.7 n.3, p.209-232, August 2003
	S. K. Gupta , V. Bhatnagar , S. K. Wasan, Architecture for knowledge discovery and knowledge management, Knowledge and Information Systems, v.7 n.3, p.310-336, March 2005
	Irene Pekerskaya , Jian Pei , Ke Wang, Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach, Journal of Intelligent Information Systems, v.27 n.3, p.215-242, November 2006
	Manolis Terrovitis , Panos Vassiliadis , Spiros Skiadopoulos , Elisa Bertino , Barbara Catania , Anna Maddalena , Stefano Rizzi, Modeling and language support for the management of pattern-bases, Data & Knowledge Engineering, v.62 n.2, p.368-397, August, 2007
	Antonin Rozsypal , Miroslav Kubat, Association mining in time-varying domains, Intelligent Data Analysis, v.9 n.3, p.273-288, May 2005
	Stephen D. Bay , Michael J. Pazzani, Detecting Group Differences: Mining Contrast Sets, Data Mining and Knowledge Discovery, v.5 n.3, p.213-246, July 2001
	Naixue Xiong , Laurence T. Yang , Yingshu Li, ODMCA: An adaptive data mining control algorithm in multicarrier networks, Computer Communications, v.32 n.3, p.560-567, February, 2009
	Ilaria Bartolini , Paolo Ciaccia , Irene Ntoutsi , Marco Patella , Yannis Theodoridis, The Panda framework for comparing patterns, Data & Knowledge Engineering, v.68 n.2, p.244-260, February, 2009
	John F. Roddick , Myra Spiliopoulou , Daniel Lister , Aaron Ceglar, Higher order mining, ACM SIGKDD Explorations Newsletter, v.10 n.1, June 2008
	Chien Chin Chen , Meng Chang Chen , Ming-Syan Chen, An adaptive threshold framework for event detection using HMM-based life profiles, ACM Transactions on Information Systems (TOIS), v.27 n.2, p.1-35, February 2009
	Mirko Böttcher , Frank Höppner , Myra Spiliopoulou, On exploiting the power of time in data mining, ACM SIGKDD Explorations Newsletter, v.10 n.2, December 2008