Distributed Decision-Tree Induction in Peer-to-Peer Systems
This paper offers a scalable and robust distributed algorithm for decision‐tree induction in large peer‐to‐peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication‐expensive and impractical because of the syn...
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| Veröffentlicht in: | Statistical analysis and data mining Jg. 1; H. 2; S. 85 - 103 |
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| Abstract | This paper offers a scalable and robust distributed algorithm for decision‐tree induction in large peer‐to‐peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication‐expensive and impractical because of the synchronization requirements. The problem becomes even more challenging in the distributed stream monitoring scenario where the decision tree needs to be updated in response to changes in the data distribution. This paper presents an alternate solution that works in a completely asynchronous manner in distributed environments and offers low communication overhead, a necessity for scalability. It also seamlessly handles changes in data and peer failures. The paper presents extensive experimental results to corroborate the theoretical claims. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008 |
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| AbstractList | This paper offers a scalable and robust distributed algorithm for decision‐tree induction in large peer‐to‐peer (P2P) environments. Computing a decision tree in such large distributed systems using standard centralized algorithms can be very communication‐expensive and impractical because of the synchronization requirements. The problem becomes even more challenging in the distributed stream monitoring scenario where the decision tree needs to be updated in response to changes in the data distribution. This paper presents an alternate solution that works in a completely asynchronous manner in distributed environments and offers low communication overhead, a necessity for scalability. It also seamlessly handles changes in data and peer failures. The paper presents extensive experimental results to corroborate the theoretical claims. Copyright © 2008 Wiley Periodicals, Inc., A Wiley Company Statistical Analy Data Mining 1: 000‐000, 2008 |
| Author | Bhaduri, Kanishka Wolff, Ran Giannella, Chris Kargupta, Hillol |
| Author_xml | – sequence: 1 givenname: Kanishka surname: Bhaduri fullname: Bhaduri, Kanishka email: kanishk1@cs.umbc.edu organization: Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA – sequence: 2 givenname: Ran surname: Wolff fullname: Wolff, Ran organization: Department of Management Information Systems, Haifa University, Haifa, 31905, Israel – sequence: 3 givenname: Chris surname: Giannella fullname: Giannella, Chris organization: Department of Computer Science, New Mexico State University, Las Cruces NM, 88003, USA – sequence: 4 givenname: Hillol surname: Kargupta fullname: Kargupta, Hillol organization: Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, Maryland, 21250, USA |
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| Cites_doi | 10.1109/ICDCS.2007.6238553 10.1109/ICDM.2004.10114 10.1137/1.9781611972719.19 10.1007/BF00116251 10.1016/j.jpdc.2007.07.011 10.1145/1082469.1082470 10.1007/s10723-007-9069-5 10.3233/HIS-2004-11-210 10.1109/TWC.2005.846971 10.1145/1233321.1233323 10.1145/1014052.1014120 10.1007/BF00058655 10.1109/90.554729 10.1145/1165555.1165569 10.1145/347090.347107 10.1016/j.ins.2005.11.007 10.1109/ICAC.2004.1301345 10.1109/TSMCB.2004.836888 10.1109/MIC.2006.74 10.4018/978-1-59904-663-1.ch007 10.1137/1.9781611972764.38 10.1023/A:1007665907178 10.1145/1081870.1081896 10.1137/1.9781611972764.14 10.1007/978-3-540-30186-8_20 10.1109/TKDE.2005.129 10.7551/mitpress/1100.003.0014 10.1145/1146381.1146399 10.1006/jpdc.2000.1694 10.1109/TKDE.2007.190714 10.1145/984622.984624 10.1145/1281192.1281296 10.1145/1150402.1150488 |
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| References_xml | – reference: F. V. Jensen, An Introduction to Bayesian Networks, London, UCL Press 1996. – reference: J. R. Quinlan, Induction of Decision Trees, Mach Learn 1(1):1986; 81-106. – reference: I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, CA, Morgan Kaufmann, 2005. – reference: I. Keidar and A. Schuster, Want scalable computing?: speculate!' SIGACT News 37(3):2006; 59-66. – reference: D. Caragea, A. Silvescu and V. Honavar, A framework for learning from distributed data using sufficient statistics and its application to learning decision trees, Int J Hybrid Intell Syst 1(1-2):2004; 80-89. – reference: M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, An introduction to variational methods for graphical models, Mach Learn 37(2):1999; 183-233. – reference: L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Belmont, Wadsworth, 1984. – reference: H. Kargupta and P. Chan, eds., Advances in Distributed and Parallel Knowledge Discovery, Menlo Park, CA, MIT Press, 2000. – reference: R. Wolff and A. Schuster, Association rule mining in peer-to-peer systems, IEEE Trans Syst Man Cybern Part B 34(6):2004; 2426-2438. – reference: S. Mukherjee and H. Kargupta, Distributed probabilistic inferencing in sensor networks using variational approximation, JPDC 68(1):2008; 78-92. – reference: P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, New Jersey, Addison-Wesley, 2006. – reference: J. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA, Morgan Kauffman, 1993. – reference: S. Datta, K. Bhaduri, C. Giannella, R. Wolff, and H. Kargupta, Distributed data mining in peer-to-peer networks, IEEE Internet Comput Spec Issu Distributed Data Mining 10(4):2006; 18-26. – reference: S. Banyopadhyay, C. Giannella, U. Maulik, H. Kargupta, K. Liu, and S. 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Kargupta, Distributed identification of top-$ l$ inner product elements and its application in a peer-to-peer network, IEEE Trans Knowl Data Eng (TKDE) 20(4):2008; 475-488. – reference: N. Li, J. Hou, and L. Sha, Design and analysis of an MST-based topology control algorithm, IEEE Trans Wireless Commun 4(3):2005; 1195-1205. – reference: D. Krivitski, A. Schuster, and R. Wolff, A local facility location algorithm for large-scale distributed systems, J Grid Comput 5(4):2007; 361-378. – reference: D. E. Hershberger and H. Kargupta, Distributed multivariate regression using wavelet-based collective data mining, JPDC, 61(3):2001; 372-400. – reference: H. Kargupta and K. Sivakumar, Existential Pleasures of Distributed Data Mining. 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| SubjectTerms | data mining decision trees peer-to-peer |
| Title | Distributed Decision-Tree Induction in Peer-to-Peer Systems |
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