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Application of Dynamic Programming Approach to Optimization of Association Rules Relative to Coverage and Length

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Konferencja
Rough Set Theory Workshop (RST’2015); (6; 29-06-2015; University of Warsaw )
Języki publikacji
EN
Abstrakty
EN
In the paper, an application of dynamic programming approach to global optimization of approximate association rules relative to coverage and length is presented. It is an extension of the dynamic programming approach to optimization of decision rules to inconsistent tables. Experimental results with data sets from UCI Machine Learning Repository are included.
Wydawca
Rocznik
Strony
87--105
Opis fizyczny
Bibliogr. 33 poz., rys., tab.
Twórcy
autor
  • Institute of Computer Science, University of Silesia, 39, Będzińska St., 41-200 Sosnowiec, Poland
Bibliografia
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  • [17] Zaki MJ, Parthasarathy S, Ogihara M, Li W. New Algorithms for Fast Discovery of Association Rules. Rochester, NY, USA; 1997. Available from: http://www.ncstrl.org:8900/ncstrl/servlet/search?formname=detail\&id=oai\%3Ancstrlh\%3Arochester_cs\%3Ancstrl.rochester_cs\%2F\%2FTR651
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  • [19] Bonates TO, Hammer PL, Kogan A. Maximum patterns in datasets. Discrete Applied Mathematics. 2008; 156(6):846–861. doi:10.1016/j.dam.2007.06.004.
  • [20] Moshkov MJ, Piliszczuk M, Zielosko B. Greedy Algorithm for Construction of Partial Association Rules. Fundam Inform. 2009;92(3):259–277. Available from: http://dl.acm.org/citation.cfm?id=1551885.1551888.
  • [21] Nguyen HS, Ślęzak D. Approximate Reducts and Association Rules - Correspondence and Complexity Results. In: Zhong N, Skowron A, Ohsuga S, editors. RSFDGrC. vol. 1711 of LNCS. Springer; 1999. p.137–145. Available from: http://dl.acm.org/citation.cfm?id=646590.697625.
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  • [23] Moshkov MJ, Piliszczuk M, Zielosko B. On Construction of Partial Association Rules. In: Wen P, Li Y, Polkowski L, Yao Y, Tsumoto S, Wang G, editors. RSKT. vol. 5589 of LNCS. Springer; 2009. p. 176–183. doi:10.1007/978-3-642-02962-2_22.
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  • [26] Zielosko B. Global Optimization of Exact Association Rules Relative to Length. In: Suraj Z, Czaja L, editors. Proceedings of 24th International Workshop, CS&P 2015. vol. 2. University of Rzeszów; 2015. p. 237–247. Available from: http://ceur-ws.org/Vol-1492/Paper_49.pdf.
  • [27] Amin T, Chikalov I, Moshkov M, Zielosko B. Dynamic programming approach to optimization of approximate decision rules. Inf Sci. 2013;221(1):403–418. Available from: http://dx.doi.org/10.1016/j.ins.2012.09.018.
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  • [29] Moshkov M, Chikalov I. On algorithm for constructing of decision trees with minimal depth. Fundam Inform. 2000;41(3):295–299. Available from: http://dl.acm.org/citation.cfm?id=343097.343100.
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  • [32] Zielosko B. Optimization of Decision Rules Relative to Coverage - Comparative Study. In: Kryszkiewicz M, Cornelis C, Ciucci D, Medina-Moreno J, Motoda H, Raś ZW, editors. JRS 2014. vol. 8537 of LNCS. Springer; 2014. p. 237–247. doi:10.1007/978-3-319-08729-0_23.
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Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c025a8c9-8240-4c2e-b616-50b78fb9ffb4
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