Sieci bayesowskie w klasteryzacji dokumentów

• Autorzy: Kłopotek M. A.
• Języki publikacji: PL

Abstrakty

PL

W niniejszej pracy przedstawiono rozszerzenia algorytmów PLSA i PHIT do grupowania dokumentów tekstowych. Główna idea rozszerzenia polega na wykorzystaniu sieci bayesowskiej typu TAN zamiast sieci naiwnej, jak ma to miejsce w algorytmach pierwotnych.

Słowa kluczowe

PL

klasteryzacja dokumentów; sieci bayesowskie; mapa dokumentów; podejmowanie decyzji

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2004
• Tom: Vol. 2(4)
• Strony: 47--82
• Opis fizyczny: Bibliogr. 38 poz., rys

Twórcy

autor

Kłopotek M. A.

• Instytut Informatyki Akademii Podlaskiej, Siedlce

Bibliografia

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• [Kłopotek 2002a] M.A.Kłopotek: A New Space-Saving Bayesian Tree Construction Method for High Dimensional Data Demonstratio Mathematica, Vol. 35, No. 3 (2002) pp. 671-684.
• [Kłopotek 2002b] M.A.Kłopotek: Space Saving Approach to Fitting Tree Distributions to High-Dimensional Sparse Data. In Proc. Sztuczna Inteligencja, 5th National Conference, Siedlce, September 2002, pp. 13-18.
• [Kłopotek 2002c] M.A.Kłopotek: A New Bayesian Tree Learning Method with Reduced Time and Space Complexity. Fundamenta Informaticae, 49(no 4)2002, IOS Press, pp. 349-367.
• [Kłopotek 2002d] M.A.Kłopotek: Minig Bayesian Networks Structure for Large Sets of Variables, in M.S.Hacid, Z.W.Ras, D.A. Zighed, Y. Kodratoff (eds): Foundations of Intelligent Systems Lecture Notes in Artificial Intelligence 2366, Springer-Verlag, pp. 114-122.
• [Kłopotek, Wierzchoń 2002a] M.A.Kłopotek, S.T.Wierzchoń: Collaborative Filtering with Bayesian Net. IN: B. Wiszniewski: Electronic Commerce. Theory and Applications Published by: Faculty of Management and Economics, Gdansk University of Technology, Poland, pp. 81-87.
• [Kłopotek, Wierzchoń 2002b] M.A.Kłopotek, S.T.Wierzchoń: Distributed enumeration protocol for valuation based systems. Zeszyty Naukowe Politechniki Białostockiej, 2002, Informatyka, pp. 83-96.
• [Kłopotek 2003a] M.A. Kłopotek: Reasoning methods in general and structured Bayesian networks. Studia Informática. Systemy i technologie informacyjne, Nr 1, 2003, pp. 1-26.
• [Kłopotek 2003b] M.A. Kłopotek: Reasoning and Learning in Extended Structured Bayesian Networks. Fundamenta Informaticae. 58(2)2003, pp.105-137.
• [Kłopotek, Sonsnowski 2003] M.A. Kłopotek, A. Sosnowski: Mixnet a wnioskowanie w sieciach bayesowskich swobodnie dyskretno-ciaglych. Z. Bubnicki, A. Grzech eds: Proc. V KRAJOWA KONFERENCJA NAUKOWA Inżynieria Wiedzy i Systemy Ekspertowe, Wrocław 11-13.6.2003 Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2003 Tom 1, 30-37.
• [Kłopotek, Woch 2003] M.A. Kłopotek, M. Woch: Very Large Bayesian Networks in Text Classification. In: Sloot, P. M.A.; Abramson, D.; Bogdanov, A.V.; Dongarra, J. J.; Zomaya, A. Y.; Gorbachev, Y.E. (Eds.): (2003) Computational Science ICCS 2003 International Conference, Melbourne, Australia and St. Petersburg, Russia, June 2-4, 2003. Proc., LNCS 2657, Springer Verlag 2003, pp. 397-406.
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• [Meila, Jordan 2000] M. Meila, M. Jordan: Learning with mixtures of trees. Journal of Machine Learning Research, Volume 1 (2000) http://citeseer.nj.nec.com/meila00learning.html
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• [Wierzchoń, Kłopotek 2002] Wierzchoń S.Т., Kłopotek M.A.: Evidential Reasoning. An Interpretative Investigation. Wydawnictwo Akademii Podlaskiej, Siedlce 2002, monography no. 44, 304 pp.