Database Mining: A Performance Perspective.

Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6): 914-925(1993)
  author    = {Rakesh Agrawal and
               Tomasz Imielinski and
               Arun N. Swami},
  title     = {Database Mining: A Performance Perspective},
  journal   = {IEEE Trans. Knowl. Data Eng.},
  volume    = {5},
  number    = {6},
  year      = {1993},
  pages     = {914-925},
  ee        = {db/journals/tkde/AgrawalIS93.html},
  bibsource = {DBLP,}


The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented. Three classes of database mining problems involving classification, associations, and sequences are described. It is argued that these problems can be uniformly viewed as requiring discovery of rules embedded in massive amounts of data. A model and some basic operations for the process of rule discovery are described. It is shown how the database mining problems considered map to this model, and how they can be solved by using the basic operations proposed. An example is given of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm is efficient in discovering classification rules and has accuracy comparable to ID3, one of the best current classifiers.

Copyright © 1993 by The Institute of Electrical and Electronic Engineers, Inc. (IEEE). Abstract used with permission.

Joint ACM SIGMOD / IEEE Computer Society Anthology

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Rakesh Agrawal, Sakti P. Ghosh, Tomasz Imielinski, Balakrishna R. Iyer, Arun N. Swami: An Interval Classifier for Database Mining Applications. VLDB 1992: 560-573 BibTeX
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Contents BibTeX

Referenced by

  1. Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos: Quantifiable Data Mining Using Ratio Rules. VLDB J. 8(3-4): 254-266(2000)
  2. Sunil Choenni: Design and Implementation of a Genetic-Based Algorithm for Data Mining. VLDB 2000: 33-42
  3. Laks V. S. Lakshmanan, Fereidoon Sadri, Subbu N. Subramanian: On Efficiently Implementing SchemaSQL on an SQL Database System. VLDB 1999: 471-482
  4. Johannes Gehrke, Venkatesh Ganti, Raghu Ramakrishnan, Wei-Yin Loh: BOAT-Optimistic Decision Tree Construction. SIGMOD Conference 1999: 169-180
  5. Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan: A Framework for Measuring Changes in Data Characteristics. PODS 1999: 126-137
  6. Mohammed Javeed Zaki, Ching-Tien Ho, Rakesh Agrawal: Parallel Classification for Data Mining on Shared-Memory Multiprocessors. ICDE 1999: 198-205
  7. Colin L. Carter, Howard J. Hamilton: Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases. IEEE Trans. Knowl. Data Eng. 10(2): 193-208(1998)
  8. Claudio Bettini, Xiaoyang Sean Wang, Sushil Jajodia, Jia-Ling Lin: Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences. IEEE Trans. Knowl. Data Eng. 10(2): 222-237(1998)
  9. Claudio Bettini, Xiaoyang Sean Wang, Sushil Jajodia: Mining Temporal Relationships with Multiple Granularities in Time Sequences. IEEE Data Eng. Bull. 21(1): 32-38(1998)
  10. Rajeev Rastogi, Kyuseok Shim: PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. VLDB 1998: 404-415
  11. Flip Korn, Alexandros Labrinidis, Yannis Kotidis, Christos Faloutsos: Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining. VLDB 1998: 582-593
  12. Johannes Gehrke, Raghu Ramakrishnan, Venkatesh Ganti: RainForest - A Framework for Fast Decision Tree Construction of Large Datasets. VLDB 1998: 416-427
  13. Soumen Chakrabarti, Sunita Sarawagi, Byron Dom: Mining Surprising Patterns Using Temporal Description Length. VLDB 1998: 606-617
  14. Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur: Dynamic Itemset Counting and Implication Rules for Market Basket Data. SIGMOD Conference 1997: 255-264
  15. Sergey Brin, Rajeev Motwani, Craig Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD Conference 1997: 265-276
  16. Brian Lent, Arun N. Swami, Jennifer Widom: Clustering Association Rules. ICDE 1997: 220-231
  17. Tadeusz Morzy, Maciej Zakrzewicz: SQL-Like Language for Database Mining. ADBIS 1997: 311-317
  18. Hongjun Lu, Rudy Setiono, Huan Liu: Effective Data Mining Using Neural Networks. IEEE Trans. Knowl. Data Eng. 8(6): 957-961(1996)
  19. David Wai-Lok Cheung, Vincent T. Y. Ng, Ada Wai-Chee Fu, Yongjian Fu: Efficient Mining of Association Rules in Distributed Databases. IEEE Trans. Knowl. Data Eng. 8(6): 911-922(1996)
  20. Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
  21. Tomasz Imielinski, Heikki Mannila: A Database Perspective on Knowledge Discovery. Commun. ACM 39(11): 58-64(1996)
  22. John C. Shafer, Rakesh Agrawal, Manish Mehta: SPRINT: A Scalable Parallel Classifier for Data Mining. VLDB 1996: 544-555
  23. Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Constructing Efficient Decision Trees by Using Optimized Numeric Association Rules. VLDB 1996: 146-155
  24. Peter G. Selfridge, Divesh Srivastava, Lynn O. Wilson: IDEA: Interactive Data Exploration and Analysis. SIGMOD Conference 1996: 24-34
  25. Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization. SIGMOD Conference 1996: 13-23
  26. Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama: Mining Optimized Association Rules for Numeric Attributes. PODS 1996: 182-191
  27. Claudio Bettini, Xiaoyang Sean Wang, Sushil Jajodia: Testing Complex Temporal Relationships Involving Multiple Granularities and Its Application to Data Mining. PODS 1996: 68-78
  28. Chung-Sheng Li, Philip S. Yu, Vittorio Castelli: HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences. ICDE 1996: 546-553
  29. Manish Mehta, Rakesh Agrawal, Jorma Rissanen: SLIQ: A Fast Scalable Classifier for Data Mining. EDBT 1996: 18-32
  30. Hongjun Lu, Rudy Setiono, Huan Liu: NeuroRule: A Connectionist Approach to Data Mining. VLDB 1995: 478-489
  31. Rakesh Agrawal, King-Ip Lin, Harpreet S. Sawhney, Kyuseok Shim: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. VLDB 1995: 490-501
  32. Maurice A. W. Houtsma, Arun N. Swami: Set-Oriented Mining for Association Rules in Relational Databases. ICDE 1995: 25-33
  33. Sarabjot S. Anand, David A. Bell, John G. Hughes: The Role of Domain Knowledge in Data Mining. CIKM 1995: 37-43
  34. Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
  35. Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos: Fast Subsequence Matching in Time-Series Databases. SIGMOD Conference 1994: 419-429
  36. Rakesh Agrawal, Michael J. Carey, Christos Faloutsos, Sakti P. Ghosh, Maurice A. W. Houtsma, Tomasz Imielinski, Balakrishna R. Iyer, A. Mahboob, H. Miranda, Ramakrishnan Srikant, Arun N. Swami: Quest: A Project on Database Mining. SIGMOD Conference 1994: 514
  37. Rakesh Agrawal: Tutorial Database Mining. PODS 1994: 75-76
  38. David A. Bell: Value-Added Databases: Knowledge Discovery and Evidential Reasoning (Invited Paper). ADBIS 1994: 2-9
  39. Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-Shapiro: Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5(6): 903-913(1993)
  40. Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216
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