메뉴 건너뛰기




Volumn 1, Issue 2, 2008, Pages 85-103

Distributed decision-tree induction in peer-to-peer systems

Author keywords

Data mining; Decision trees; Peer to peer

Indexed keywords

DATA MINING; DISTRIBUTED COMPUTER SYSTEMS; PEER TO PEER NETWORKS;

EID: 61449249027     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10006     Document Type: Article
Times cited : (65)

References (49)
  • 1
    • 33744584654 scopus 로고
    • Induction of Decision Trees
    • J. R. Quinlan, Induction of Decision Trees, Mach Learn 1(1) (1986), 81-106.
    • (1986) Mach Learn , vol.1 , Issue.1 , pp. 81-106
    • Quinlan, J.R.1
  • 4
    • 38049149862 scopus 로고    scopus 로고
    • Client-side web mining for community formation in peer-to-peer environments
    • K. Liu, K. Bhaduri, K. Das, P. Nguyen, and H. Kargupta, Client-side web mining for community formation in peer-to-peer environments, SIGKDD Explor 8(2) (2006,), 11-20.
    • (2006) SIGKDD Explor , vol.8 , Issue.2 , pp. 11-20
    • Liu, K.1    Bhaduri, K.2    Das, K.3    Nguyen, P.4    Kargupta, H.5
  • 5
    • 40349093215 scopus 로고    scopus 로고
    • Distributed identification of top-$l$ inner product elements and its application in a peer-to-peer network
    • K. Das, K. Bhaduri, K. Liu, and H. 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.
    • (2008) IEEE Trans Knowl Data Eng (TKDE) , vol.20 , Issue.4 , pp. 475-488
    • Das, K.1    Bhaduri, K.2    Liu, K.3    Kargupta, H.4
  • 6
    • 10044279537 scopus 로고    scopus 로고
    • Association rule mining in peer-to-peer systems
    • 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.
    • (2004) IEEE Trans Syst Man Cybern Part B , vol.34 , Issue.6 , pp. 2426-2438
    • Wolff, R.1    Schuster, A.2
  • 7
    • 12244293654 scopus 로고    scopus 로고
    • k-TTP: a new privacy model for large-scale distributed environments
    • New York
    • B. Gilburd, A. Schuster, and R. Wolff, k-TTP: a new privacy model for large-scale distributed environments, In Proceedings of SIGKDD'04, New York, 2004, 563-568.
    • (2004) Proceedings of SIGKDD'04 , pp. 563-568
    • Gilburd, B.1    Schuster, A.2    Wolff, R.3
  • 8
    • 77949518289 scopus 로고    scopus 로고
    • Collaborative use of features in a distributed system for the organization of music collections
    • Shen, Shepherd, Cui, and Liu, Eds. Information Science Reference
    • I. Mierswa, K. Morik, and M. Wurst, Collaborative use of features in a distributed system for the organization of music collections, In Intelligent Music Information Systems: Tools and Methodologies, Shen, Shepherd, Cui, and Liu, Eds. Information Science Reference, 2007, 147-175.
    • (2007) Intelligent Music Information Systems: Tools and Methodologies , pp. 147-175
    • Mierswa, I.1    Morik, K.2    Wurst, M.3
  • 10
  • 16
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman, Bagging predictors, Mach Learn 2 (1996), 123-140.
    • (1996) Mach Learn , vol.2 , pp. 123-140
    • Breiman, L.1
  • 17
    • 0003660631 scopus 로고    scopus 로고
    • Additive Logistic Regression: A Statistical View of Boosting
    • Stanford University, Technical Report
    • J. Friedman, T. Hastie, and R. Tibshirani, Additive Logistic Regression: A Statistical View of Boosting, Department of Statistics, Stanford University, Technical Report, 1998.
    • (1998) Department of Statistics
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 20
    • 19544373409 scopus 로고    scopus 로고
    • A framework for learning from distributed data using sufficient statistics and its application to learning decision trees
    • 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.
    • (2004) Int J Hybrid Intell Syst , vol.1 , Issue.1-2 , pp. 80-89
    • Caragea, D.1    Silvescu, A.2    Honavar, V.3
  • 21
    • 19544380393 scopus 로고    scopus 로고
    • Communication efficient construction of decision trees over heterogeneously distributed data
    • Brighton
    • C. Giannella, K. Liu, T. Olsen, and H. Kargupta, Communication efficient construction of decision trees over heterogeneously distributed data, In Proceedings of ICDM'04, Brighton, 2004, 67-74.
    • (2004) Proceedings of ICDM'04 , pp. 67-74
    • Giannella, C.1    Liu, K.2    Olsen, T.3    Kargupta, H.4
  • 23
    • 32344432101 scopus 로고    scopus 로고
    • A distributed learning framework for heterogeneous data sources
    • S. Merugu and J. Ghosh, A distributed learning framework for heterogeneous data sources, In Proceedings of KDD'05, 2005, 208-217.
    • (2005) Proceedings of KDD'05 , pp. 208-217
    • Merugu, S.1    Ghosh, J.2
  • 24
    • 0004580852 scopus 로고    scopus 로고
    • A fourier analysis-based approach to learn classifier from distributed heterogeneous data
    • Chicago, IL, April
    • B. Park, R. Ayyagari, and H. Kargupta, A fourier analysis-based approach to learn classifier from distributed heterogeneous data, In Proceedings of SDM'01, Chicago, IL, April 2001.
    • (2001) Proceedings of SDM'01
    • Park, B.1    Ayyagari, R.2    Kargupta, H.3
  • 25
    • 0346250708 scopus 로고    scopus 로고
    • Distributed multivariate regression using wavelet-based collective data mining
    • D. E. Hershberger and H. Kargupta, Distributed multivariate regression using wavelet-based collective data mining, JPDC, 61(3) (2001), 372-400.
    • (2001) JPDC , vol.61 , Issue.3 , pp. 372-400
    • Hershberger, D.E.1    Kargupta, H.2
  • 26
    • 35048830039 scopus 로고    scopus 로고
    • Distributed regression: an efficient framework for modeling sensor network data
    • Berkeley
    • C. Guestrin, P. Bodi, R. Thibau, M. Paski, and S. Madden, Distributed regression: an efficient framework for modeling sensor network data, In Proceedings of IPSN'04, Berkeley, 2004, 1-10.
    • (2004) Proceedings of IPSN'04 , pp. 1-10
    • Guestrin, C.1    Bodi, P.2    Thibau, R.3    Paski, M.4    Madden, S.5
  • 28
    • 33646164394 scopus 로고    scopus 로고
    • Clustering distributed data streams in peer-to-peer environments
    • S. Banyopadhyay, C. Giannella, U. Maulik, H. Kargupta, K. Liu, and S. Datta, Clustering distributed data streams in peer-to-peer environments, Inf Sci 176(14) (2006), 1952-1985.
    • (2006) Inf Sci , vol.176 , Issue.14 , pp. 1952-1985
    • Banyopadhyay, S.1    Giannella, C.2    Maulik, U.3    Kargupta, H.4    Liu, K.5    Datta, S.6
  • 29
    • 40349087928 scopus 로고    scopus 로고
    • Uniform data sampling from a peer-to-peer network
    • Toronto
    • S. Datta and H. Kargupta, Uniform data sampling from a peer-to-peer network, In Proceedings of ICDCS'02, Toronto, 2007, 50.
    • (2007) Proceedings of ICDCS'02 , pp. 50
    • Datta, S.1    Kargupta, H.2
  • 30
    • 36749042796 scopus 로고    scopus 로고
    • Distributed probabilistic inferencing in sensor networks using variational approximation
    • S. Mukherjee and H. Kargupta, Distributed probabilistic inferencing in sensor networks using variational approximation, JPDC 68(1) (2008), 78-92.
    • (2008) JPDC , vol.68 , Issue.1 , pp. 78-92
    • Mukherjee, S.1    Kargupta, H.2
  • 31
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • 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.
    • (1999) Mach Learn , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 33
    • 33745479072 scopus 로고    scopus 로고
    • K-means clustering over large, dynamic networks
    • Maryland
    • S. Datta, C. Giannella, and H. Kargupta, K-means clustering over large, dynamic networks, In Proceedings of SDM'06, Maryland, 2006, 153-164.
    • (2006) Proceedings of SDM'06 , pp. 153-164
    • Datta, S.1    Giannella, C.2    Kargupta, H.3
  • 34
  • 36
    • 35348989678 scopus 로고    scopus 로고
    • A local facility location algorithm for large-scale distributed systems
    • 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.
    • (2007) J Grid Comput , vol.5 , Issue.4 , pp. 361-378
    • Krivitski, D.1    Schuster, A.2    Wolff, R.3
  • 38
    • 36849051408 scopus 로고    scopus 로고
    • Distributed classification in peer-to-peer networks
    • P. Luo, H. Xiong, K. L̈u, and Z. Shi, Distributed classification in peer-to-peer networks, In Proceedings of SIGKDD'07, 2007, 968-976.
    • (2007) Proceedings of SIGKDD'07 , pp. 968-976
    • Luo, P.1    Xiong, H.2    L̈u, K.3    Shi, Z.4
  • 39
  • 40
    • 33745461062 scopus 로고    scopus 로고
    • Local L2 thresholding based data mining in peer-to-peer systems
    • R. Wolff, K. Bhaduri, and H. Kargupta, Local L2 thresholding based data mining in peer-to-peer systems, In Proceedings of SDM'06, 2006, 428-439.
    • (2006) Proceedings of SDM'06 , pp. 428-439
    • Wolff, R.1    Bhaduri, K.2    Kargupta, H.3
  • 41
    • 0031077360 scopus 로고    scopus 로고
    • A path-finding algorithm for loop-free routing
    • J. Garcia-Luna-Aceves and S. Murthy, A path-finding algorithm for loop-free routing, IEEE Trans Network 5(1) (1997), 148-160.
    • (1997) IEEE Trans Network , vol.5 , Issue.1 , pp. 148-160
    • Garcia-Luna-Aceves, J.1    Murthy, S.2
  • 42
    • 56149086538 scopus 로고    scopus 로고
    • Heartbeat based fault diagnosis for mobile ad-hoc network
    • Phuket, Thailand
    • P. M. Khilar and S. Mahapatra, Heartbeat based fault diagnosis for mobile ad-hoc network, In Proceedings of IASTED'07, Phuket, Thailand, 2007, 194-199.
    • (2007) Proceedings of IASTED'07 , pp. 194-199
    • Khilar, P.M.1    Mahapatra, S.2
  • 43
    • 33748694578 scopus 로고    scopus 로고
    • A local algorithm for Ad Hoc majority voting via charge fusion
    • Tokyo, Japan
    • Y. Birk, L. Liss, A. Schuster, and R.Wolff, A local algorithm for Ad Hoc majority voting via charge fusion, In Proceedings of ICDCS'04, Tokyo, Japan, 2004.
    • (2004) Proceedings of ICDCS'04
    • Birk, Y.1    Liss, L.2    Schuster, A.3    Wolff, R.4
  • 44
    • 18844404710 scopus 로고    scopus 로고
    • Design and analysis of an MST-based topology control algorithm
    • N. Li, J. Hou, and L. Sha, Design and analysis of an MST-based topology control algorithm, IEEE TransWireless Commun 4(3) (2005), 1195-1205.
    • (2005) IEEE TransWireless Commun , vol.4 , Issue.3 , pp. 1195-1205
    • Li, N.1    Hou, J.2    Sha, L.3
  • 46
    • 35348989678 scopus 로고    scopus 로고
    • A local facility location algorithm for large-scale distributed systems
    • 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.
    • (2007) J Grid Comput , vol.5 , Issue.4 , pp. 361-378
    • Krivitski, D.1    Schuster, A.2    Wolff, R.3
  • 49


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.