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Efficient algorithms for constructing decision trees with constraints
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Boston, Massachusetts, United States
Pages: 335 - 339  
Year of Publication: 2000
ISBN:1-58113-233-6
Authors
Minos Garofalakis  Bell Laboratories
Dongjoon Hyun  Korea Advanced Institute of Science and Technology and Advanced Information Technology Research Centre
Rajeev Rastogi  Bell Laboratories
Kyuseok Shim  Korea Advanced Institute of Science and Technology and Advanced Information Technology Research Centre
Sponsors
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 9,   Downloads (12 Months): 57,   Citation Count: 8
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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
Hussein Almuallim, An efficient algorithm for optimal pruning of decision trees, Artificial Intelligence, v.83 n.2, p.347-362, June 1996  [doi>10.1016/0004-3702(95)00060-7]
 
2
Marko Bohanec , Ivan Bratko, Trading Accuracy for Simplicity in Decision Trees, Machine Learning, v.15 n.3, p.223-250, June 1994  [doi>10.1023/A:1022685808937]
 
3
Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone. "Classification and Regression Trees". Chapman gz Hall, 1984.
 
4
Minos Garofalakis, Dongjoon Hyun, Rajeev Rastogi, and Kyuseok Shim. "Efficient Algorithms for Constructing Decision Trees with Constraints". March 2000. Bell Laboratories Tech. Memorandum.
5
Johannes Gehrke , Venkatesh Ganti , Raghu Ramakrishnan , Wei-Yin Loh, BOAT—optimistic decision tree construction, Proceedings of the 1999 ACM SIGMOD international conference on Management of data, p.169-180, May 31-June 03, 1999, Philadelphia, Pennsylvania, United States
 
6
Manish Mehta, Jorma Rissanen, and Rakesh Agrawal. MDL-based decision tree pruning. In Int'l Conference on Knowledge Discovery in Databases and Data Mining (KDD-95), Montreal, Canada, August 1995.
 
7
J. R. Quinlan , R. L. Rivest, Inferring decision trees using the minimum description length principle, Information and Computation, v.80 n.3, p.227-248, Mar. 1989  [doi>10.1016/0890-5401(89)90010-2]
 
8
J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
 
9
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
 
10
J. Rissanen. Modeling by shortest data description. Automatica, 14:465-471, 1978.
 
11
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

CITED BY  8
Kai-Uwe Sattler , Oliver Dunemann, SQL database primitives for decision tree classifiers, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA
Mohammed J. Zaki , Charu C. Aggarwal, XRules: An effective algorithm for structural classification of XML data, Machine Learning, v.62 n.1-2, p.137-170, February 2006
Kweku-Muata Osei-Bryson, Evaluation of decision trees: a multi-criteria approach, Computers and Operations Research, v.31 n.11, p.1933-1945, September 2004
M. J. Aitkenhead, A co-evolving decision tree classification method, Expert Systems with Applications: An International Journal, v.34 n.1, p.18-25, January, 2008
Kweku-Muata Osei-Bryson, Post-pruning in decision tree induction using multiple performance measures, Computers and Operations Research, v.34 n.11, p.3331-3345, November, 2007
Minos Garofalakis , Rajeev Rastogi, Scalable data mining with model constraints, ACM SIGKDD Explorations Newsletter, v.2 n.2, p.39-48, Dec. 2000
Kweku-Muata Osei-Bryson, Post-pruning in regression tree induction: An integrated approach, Expert Systems with Applications: An International Journal, v.34 n.2, p.1481-1490, February, 2008
Ulrich Rückert , Luc De Raedt, An experimental evaluation of simplicity in rule learning, Artificial Intelligence, v.172 n.1, p.19-28, January, 2008

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory
          Subjects: Trees

  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems
          Subjects: Decision support (e.g., MIS)

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation


General Terms:
Algorithms, Design, Management, Performance, Theory


Keywords:
classification, decision tree

Collaborative Colleagues:
Minos Garofalakis: colleagues
Dongjoon Hyun: colleagues
Rajeev Rastogi: colleagues
Kyuseok Shim: colleagues

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